1 files changed, 2638 insertions, 16 deletions

diff --git a/python/notebooks/Allocation Reports.ipynb b/python/notebooks/Allocation Reports.ipynb
index bddcef63..727a1aa8 100644
--- a/python/notebooks/Allocation Reports.ipynb
+++ b/python/notebooks/Allocation Reports.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 37,
    "metadata": {
     "collapsed": true
    },
@@ -10,21 +10,22 @@
    "source": [
     "import datetime\n",
     "import pandas.tseries.offsets as off\n",
-    "import globeop_reports as go"
+    "import globeop_reports as go\n",
+    "import pandas as pd"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Timestamp('2017-10-31 00:00:00')"
+       "Timestamp('2017-11-30 00:00:00')"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -39,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -822,7 +823,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\" 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+       "<img 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\" width=\"600\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -833,16 +834,29 @@
     }
    ],
    "source": [
-    "pnl_alloc = go.alloc(report_date, 'pnl')\n",
-    "go.pnl_alloc_plot(pnl_alloc)"
+    "pnl_alloc = go.alloc('pnl')\n",
+    "alloc = pnl_alloc.xs(report_date)\n",
+    "go.pnl_alloc_plot(alloc)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/edwin/projects/code/python/globeop_reports.py:146: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  \n"
+     ]
+    },
+    {
      "data": {
       "application/javascript": [
        "/* Put everything inside the global mpl namespace */\n",
@@ -1622,7 +1636,7 @@
     {
      "data": {
       "text/html": [
-       "<img 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\" 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+       "<img 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\" width=\"800\">"
       ],
       "text/plain": [
        "<IPython.core.display.HTML object>"
@@ -1634,22 +1648,23 @@
    ],
    "source": [
     "#Capital Allocation\n",
-    "cap_alloc = go.alloc(report_date, 'capital')\n",
-    "go.cap_alloc_plot(cap_alloc)"
+    "cap_alloc = go.alloc('capital')\n",
+    "alloc1 = cap_alloc.xs(report_date)\n",
+    "go.cap_alloc_plot(alloc1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.5155553893824326"
+       "0.507475204467614"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1661,6 +1676,2613 @@
   },
   {
    "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>port</th>\n",
+       "      <th>CASH</th>\n",
+       "      <th>CLO</th>\n",
+       "      <th>MORTGAGES</th>\n",
+       "      <th>STRUCTURED</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>periodenddate</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2013-01-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-02-28</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-03-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-04-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-05-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-06-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-07-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-08-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-09-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-10-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-11-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-12-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-02-28</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-03-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-04-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-05-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-06-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-07-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-08-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-10-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-01-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-02-28</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-03-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-04-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-05-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-06-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-07-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-08-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-10-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>56.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-11-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-12-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-01-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-02-29</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-03-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-04-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-05-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-06-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-07-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-08-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-09-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>53.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-10-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-11-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>14.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-01-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-02-28</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-03-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>69.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-04-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-05-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-06-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-07-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>64.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-08-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>63.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-09-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-10-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>66.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-11-30</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>65.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "port           CASH  CLO  MORTGAGES  STRUCTURED\n",
+       "periodenddate                                  \n",
+       "2013-01-31      1.0  NaN        3.0         1.0\n",
+       "2013-02-28      1.0  1.0        9.0         3.0\n",
+       "2013-03-31      1.0  3.0       17.0         2.0\n",
+       "2013-04-30      1.0  3.0       20.0         2.0\n",
+       "2013-05-31      1.0  2.0       23.0         2.0\n",
+       "2013-06-30      1.0  2.0       27.0         4.0\n",
+       "2013-07-31      1.0  2.0       27.0         4.0\n",
+       "2013-08-31      1.0  2.0       33.0         4.0\n",
+       "2013-09-30      1.0  2.0       34.0         4.0\n",
+       "2013-10-31      1.0  3.0       30.0         4.0\n",
+       "2013-11-30      1.0  3.0       33.0         4.0\n",
+       "2013-12-31      1.0  4.0       29.0         4.0\n",
+       "2014-01-31      1.0  4.0       25.0         4.0\n",
+       "2014-02-28      1.0  5.0       23.0         5.0\n",
+       "2014-03-31      1.0  5.0       27.0         5.0\n",
+       "2014-04-30      1.0  5.0       24.0         5.0\n",
+       "2014-05-31      1.0  6.0       21.0         5.0\n",
+       "2014-06-30      1.0  7.0       22.0         7.0\n",
+       "2014-07-31      1.0  6.0       17.0         6.0\n",
+       "2014-08-31      1.0  6.0       16.0         6.0\n",
+       "2014-09-30      1.0  6.0       15.0         6.0\n",
+       "2014-10-31      1.0  6.0       18.0         7.0\n",
+       "2014-11-30      1.0  6.0       19.0         7.0\n",
+       "2014-12-31      1.0  6.0       18.0         8.0\n",
+       "2015-01-31      1.0  6.0       23.0         9.0\n",
+       "2015-02-28      1.0  6.0       27.0         9.0\n",
+       "2015-03-31      1.0  6.0       26.0        10.0\n",
+       "2015-04-30      1.0  6.0       35.0        10.0\n",
+       "2015-05-31      1.0  6.0       43.0        10.0\n",
+       "2015-06-30      1.0  7.0       40.0        14.0\n",
+       "2015-07-31      1.0  7.0       45.0        14.0\n",
+       "2015-08-31      1.0  7.0       45.0        14.0\n",
+       "2015-09-30      1.0  7.0       56.0        14.0\n",
+       "2015-10-31      1.0  7.0       56.0        14.0\n",
+       "2015-11-30      1.0  7.0       54.0        14.0\n",
+       "2015-12-31      1.0  7.0       54.0        14.0\n",
+       "2016-01-31      1.0  9.0       60.0        14.0\n",
+       "2016-02-29      1.0  9.0       59.0        14.0\n",
+       "2016-03-31      1.0  9.0       60.0        14.0\n",
+       "2016-04-30      1.0  7.0       64.0        14.0\n",
+       "2016-05-31      1.0  5.0       65.0        14.0\n",
+       "2016-06-30      1.0  5.0       67.0        14.0\n",
+       "2016-07-31      1.0  5.0       67.0        14.0\n",
+       "2016-08-31      1.0  3.0       58.0        14.0\n",
+       "2016-09-30      1.0  2.0       53.0        14.0\n",
+       "2016-10-31      1.0  1.0       64.0        14.0\n",
+       "2016-11-30      1.0  1.0       60.0        14.0\n",
+       "2016-12-31      1.0  1.0       63.0        14.0\n",
+       "2017-01-31      1.0  1.0       69.0        12.0\n",
+       "2017-02-28      1.0  NaN       66.0        12.0\n",
+       "2017-03-31      1.0  1.0       69.0        11.0\n",
+       "2017-04-30      1.0  1.0       68.0        11.0\n",
+       "2017-05-31      1.0  NaN       63.0        11.0\n",
+       "2017-06-30      1.0  1.0       64.0         7.0\n",
+       "2017-07-31      1.0  1.0       64.0         7.0\n",
+       "2017-08-31      1.0  1.0       63.0         7.0\n",
+       "2017-09-30      1.0  1.0       68.0         7.0\n",
+       "2017-10-31      1.0  1.0       66.0         7.0\n",
+       "2017-11-30      1.0  1.0       65.0         7.0\n",
+       "2017-12-31      1.0  1.0       65.0         7.0"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Number of bond positions by strategy by month - and copy to clipboard\n",
+    "go.num_bond_by_strat()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>buysell</th>\n",
+       "      <th>False</th>\n",
+       "      <th>True</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2013-01-31</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-02-28</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>17.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-03-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-04-30</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-05-31</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-06-30</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>17.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-07-31</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-08-31</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-09-30</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-10-31</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-11-30</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-12-31</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-31</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-02-28</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-03-31</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-04-30</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-05-31</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-06-30</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-07-31</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-08-31</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-30</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-10-31</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-30</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-31</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-01-31</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-02-28</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-03-31</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-04-30</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>15.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-05-31</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-06-30</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-07-31</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-08-31</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-30</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-10-31</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-11-30</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-12-31</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-01-31</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-02-29</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-03-31</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-04-30</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-05-31</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-06-30</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-07-31</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-08-31</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-09-30</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-10-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>13.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-11-30</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-01-31</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-02-28</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-03-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-04-30</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-05-31</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-06-30</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-07-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-08-31</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-09-30</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-10-31</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-11-30</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "buysell     False  True \n",
+       "2013-01-31    NaN    4.0\n",
+       "2013-02-28    3.0   17.0\n",
+       "2013-03-31    1.0   11.0\n",
+       "2013-04-30    6.0   10.0\n",
+       "2013-05-31   12.0   15.0\n",
+       "2013-06-30   11.0   17.0\n",
+       "2013-07-31    6.0    7.0\n",
+       "2013-08-31    4.0   10.0\n",
+       "2013-09-30   13.0   15.0\n",
+       "2013-10-31   10.0   10.0\n",
+       "2013-11-30    6.0   10.0\n",
+       "2013-12-31    6.0    4.0\n",
+       "2014-01-31   10.0    6.0\n",
+       "2014-02-28    6.0    7.0\n",
+       "2014-03-31    2.0    7.0\n",
+       "2014-04-30    4.0    2.0\n",
+       "2014-05-31    6.0    6.0\n",
+       "2014-06-30    2.0    5.0\n",
+       "2014-07-31    6.0    1.0\n",
+       "2014-08-31    3.0    2.0\n",
+       "2014-09-30    6.0    5.0\n",
+       "2014-10-31    4.0    7.0\n",
+       "2014-11-30    2.0    3.0\n",
+       "2014-12-31    4.0    4.0\n",
+       "2015-01-31    NaN    9.0\n",
+       "2015-02-28    5.0   10.0\n",
+       "2015-03-31    5.0    6.0\n",
+       "2015-04-30    3.0   15.0\n",
+       "2015-05-31    5.0   13.0\n",
+       "2015-06-30    8.0   13.0\n",
+       "2015-07-31    4.0   12.0\n",
+       "2015-08-31    8.0    9.0\n",
+       "2015-09-30    2.0   13.0\n",
+       "2015-10-31   12.0    8.0\n",
+       "2015-11-30    5.0    3.0\n",
+       "2015-12-31    3.0    NaN\n",
+       "2016-01-31    3.0   12.0\n",
+       "2016-02-29    2.0    2.0\n",
+       "2016-03-31    4.0    5.0\n",
+       "2016-04-30    4.0    7.0\n",
+       "2016-05-31    2.0    4.0\n",
+       "2016-06-30    6.0   10.0\n",
+       "2016-07-31    0.0    0.0\n",
+       "2016-08-31   17.0   11.0\n",
+       "2016-09-30    8.0    3.0\n",
+       "2016-10-31    1.0   13.0\n",
+       "2016-11-30    6.0    3.0\n",
+       "2016-12-31    1.0    4.0\n",
+       "2017-01-31    NaN    7.0\n",
+       "2017-02-28    5.0    2.0\n",
+       "2017-03-31    1.0    7.0\n",
+       "2017-04-30    3.0    3.0\n",
+       "2017-05-31    7.0    3.0\n",
+       "2017-06-30    3.0    5.0\n",
+       "2017-07-31    1.0    1.0\n",
+       "2017-08-31    3.0    3.0\n",
+       "2017-09-30    NaN    5.0\n",
+       "2017-10-31    3.0    4.0\n",
+       "2017-11-30    3.0    2.0"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Number of bond trades by direction by month - and copy to clipboard\n",
+    "go.num_bond_trades()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "df = cap_alloc.endbooknav.groupby('periodenddate').apply(lambda x: x/x.sum())\n",
+    "df = df.unstack().groupby(pd.TimeGrouper('M')).apply(lambda df: df.loc[df.index[-1]])\n",
+    "df = go.shift_cash(datetime.date(2017,11,30), -2096454, df, 'Curve')\n",
+    "temp = df.iloc[-1].sort_values(ascending=False)\n",
+    "df = df.reindex_axis(temp.index, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/javascript": [
+       "/* Put everything inside the global mpl namespace */\n",
+       "window.mpl = {};\n",
+       "\n",
+       "\n",
+       "mpl.get_websocket_type = function() {\n",
+       "    if (typeof(WebSocket) !== 'undefined') {\n",
+       "        return WebSocket;\n",
+       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
+       "        return MozWebSocket;\n",
+       "    } else {\n",
+       "        alert('Your browser does not have WebSocket support.' +\n",
+       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
+       "              'Firefox 4 and 5 are also supported but you ' +\n",
+       "              'have to enable WebSockets in about:config.');\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
+       "    this.id = figure_id;\n",
+       "\n",
+       "    this.ws = websocket;\n",
+       "\n",
+       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
+       "\n",
+       "    if (!this.supports_binary) {\n",
+       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
+       "        if (warnings) {\n",
+       "            warnings.style.display = 'block';\n",
+       "            warnings.textContent = (\n",
+       "                \"This browser does not support binary websocket messages. \" +\n",
+       "                    \"Performance may be slow.\");\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    this.imageObj = new Image();\n",
+       "\n",
+       "    this.context = undefined;\n",
+       "    this.message = undefined;\n",
+       "    this.canvas = undefined;\n",
+       "    this.rubberband_canvas = undefined;\n",
+       "    this.rubberband_context = undefined;\n",
+       "    this.format_dropdown = undefined;\n",
+       "\n",
+       "    this.image_mode = 'full';\n",
+       "\n",
+       "    this.root = $('<div/>');\n",
+       "    this._root_extra_style(this.root)\n",
+       "    this.root.attr('style', 'display: inline-block');\n",
+       "\n",
+       "    $(parent_element).append(this.root);\n",
+       "\n",
+       "    this._init_header(this);\n",
+       "    this._init_canvas(this);\n",
+       "    this._init_toolbar(this);\n",
+       "\n",
+       "    var fig = this;\n",
+       "\n",
+       "    this.waiting = false;\n",
+       "\n",
+       "    this.ws.onopen =  function () {\n",
+       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
+       "            fig.send_message(\"send_image_mode\", {});\n",
+       "            if (mpl.ratio != 1) {\n",
+       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
+       "            }\n",
+       "            fig.send_message(\"refresh\", {});\n",
+       "        }\n",
+       "\n",
+       "    this.imageObj.onload = function() {\n",
+       "            if (fig.image_mode == 'full') {\n",
+       "                // Full images could contain transparency (where diff images\n",
+       "                // almost always do), so we need to clear the canvas so that\n",
+       "                // there is no ghosting.\n",
+       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "            }\n",
+       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
+       "        };\n",
+       "\n",
+       "    this.imageObj.onunload = function() {\n",
+       "        this.ws.close();\n",
+       "    }\n",
+       "\n",
+       "    this.ws.onmessage = this._make_on_message_function(this);\n",
+       "\n",
+       "    this.ondownload = ondownload;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_header = function() {\n",
+       "    var titlebar = $(\n",
+       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
+       "        'ui-helper-clearfix\"/>');\n",
+       "    var titletext = $(\n",
+       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
+       "        'text-align: center; padding: 3px;\"/>');\n",
+       "    titlebar.append(titletext)\n",
+       "    this.root.append(titlebar);\n",
+       "    this.header = titletext[0];\n",
+       "}\n",
+       "\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_canvas = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var canvas_div = $('<div/>');\n",
+       "\n",
+       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
+       "\n",
+       "    function canvas_keyboard_event(event) {\n",
+       "        return fig.key_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
+       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
+       "    this.canvas_div = canvas_div\n",
+       "    this._canvas_extra_style(canvas_div)\n",
+       "    this.root.append(canvas_div);\n",
+       "\n",
+       "    var canvas = $('<canvas/>');\n",
+       "    canvas.addClass('mpl-canvas');\n",
+       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
+       "\n",
+       "    this.canvas = canvas[0];\n",
+       "    this.context = canvas[0].getContext(\"2d\");\n",
+       "\n",
+       "    var backingStore = this.context.backingStorePixelRatio ||\n",
+       "\tthis.context.webkitBackingStorePixelRatio ||\n",
+       "\tthis.context.mozBackingStorePixelRatio ||\n",
+       "\tthis.context.msBackingStorePixelRatio ||\n",
+       "\tthis.context.oBackingStorePixelRatio ||\n",
+       "\tthis.context.backingStorePixelRatio || 1;\n",
+       "\n",
+       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
+       "\n",
+       "    var rubberband = $('<canvas/>');\n",
+       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
+       "\n",
+       "    var pass_mouse_events = true;\n",
+       "\n",
+       "    canvas_div.resizable({\n",
+       "        start: function(event, ui) {\n",
+       "            pass_mouse_events = false;\n",
+       "        },\n",
+       "        resize: function(event, ui) {\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "        stop: function(event, ui) {\n",
+       "            pass_mouse_events = true;\n",
+       "            fig.request_resize(ui.size.width, ui.size.height);\n",
+       "        },\n",
+       "    });\n",
+       "\n",
+       "    function mouse_event_fn(event) {\n",
+       "        if (pass_mouse_events)\n",
+       "            return fig.mouse_event(event, event['data']);\n",
+       "    }\n",
+       "\n",
+       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
+       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
+       "    // Throttle sequential mouse events to 1 every 20ms.\n",
+       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
+       "\n",
+       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
+       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
+       "\n",
+       "    canvas_div.on(\"wheel\", function (event) {\n",
+       "        event = event.originalEvent;\n",
+       "        event['data'] = 'scroll'\n",
+       "        if (event.deltaY < 0) {\n",
+       "            event.step = 1;\n",
+       "        } else {\n",
+       "            event.step = -1;\n",
+       "        }\n",
+       "        mouse_event_fn(event);\n",
+       "    });\n",
+       "\n",
+       "    canvas_div.append(canvas);\n",
+       "    canvas_div.append(rubberband);\n",
+       "\n",
+       "    this.rubberband = rubberband;\n",
+       "    this.rubberband_canvas = rubberband[0];\n",
+       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
+       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
+       "\n",
+       "    this._resize_canvas = function(width, height) {\n",
+       "        // Keep the size of the canvas, canvas container, and rubber band\n",
+       "        // canvas in synch.\n",
+       "        canvas_div.css('width', width)\n",
+       "        canvas_div.css('height', height)\n",
+       "\n",
+       "        canvas.attr('width', width * mpl.ratio);\n",
+       "        canvas.attr('height', height * mpl.ratio);\n",
+       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
+       "\n",
+       "        rubberband.attr('width', width);\n",
+       "        rubberband.attr('height', height);\n",
+       "    }\n",
+       "\n",
+       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
+       "    // upon first draw.\n",
+       "    this._resize_canvas(600, 600);\n",
+       "\n",
+       "    // Disable right mouse context menu.\n",
+       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
+       "        return false;\n",
+       "    });\n",
+       "\n",
+       "    function set_focus () {\n",
+       "        canvas.focus();\n",
+       "        canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    window.setTimeout(set_focus, 100);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) {\n",
+       "            // put a spacer in here.\n",
+       "            continue;\n",
+       "        }\n",
+       "        var button = $('<button/>');\n",
+       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
+       "                        'ui-button-icon-only');\n",
+       "        button.attr('role', 'button');\n",
+       "        button.attr('aria-disabled', 'false');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "\n",
+       "        var icon_img = $('<span/>');\n",
+       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
+       "        icon_img.addClass(image);\n",
+       "        icon_img.addClass('ui-corner-all');\n",
+       "\n",
+       "        var tooltip_span = $('<span/>');\n",
+       "        tooltip_span.addClass('ui-button-text');\n",
+       "        tooltip_span.html(tooltip);\n",
+       "\n",
+       "        button.append(icon_img);\n",
+       "        button.append(tooltip_span);\n",
+       "\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    var fmt_picker_span = $('<span/>');\n",
+       "\n",
+       "    var fmt_picker = $('<select/>');\n",
+       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
+       "    fmt_picker_span.append(fmt_picker);\n",
+       "    nav_element.append(fmt_picker_span);\n",
+       "    this.format_dropdown = fmt_picker[0];\n",
+       "\n",
+       "    for (var ind in mpl.extensions) {\n",
+       "        var fmt = mpl.extensions[ind];\n",
+       "        var option = $(\n",
+       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
+       "        fmt_picker.append(option)\n",
+       "    }\n",
+       "\n",
+       "    // Add hover states to the ui-buttons\n",
+       "    $( \".ui-button\" ).hover(\n",
+       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
+       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
+       "    );\n",
+       "\n",
+       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
+       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
+       "    // which will in turn request a refresh of the image.\n",
+       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_message = function(type, properties) {\n",
+       "    properties['type'] = type;\n",
+       "    properties['figure_id'] = this.id;\n",
+       "    this.ws.send(JSON.stringify(properties));\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.send_draw_message = function() {\n",
+       "    if (!this.waiting) {\n",
+       "        this.waiting = true;\n",
+       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    var format_dropdown = fig.format_dropdown;\n",
+       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
+       "    fig.ondownload(fig, format);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
+       "    var size = msg['size'];\n",
+       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
+       "        fig._resize_canvas(size[0], size[1]);\n",
+       "        fig.send_message(\"refresh\", {});\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
+       "    var x0 = msg['x0'] / mpl.ratio;\n",
+       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
+       "    var x1 = msg['x1'] / mpl.ratio;\n",
+       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
+       "    x0 = Math.floor(x0) + 0.5;\n",
+       "    y0 = Math.floor(y0) + 0.5;\n",
+       "    x1 = Math.floor(x1) + 0.5;\n",
+       "    y1 = Math.floor(y1) + 0.5;\n",
+       "    var min_x = Math.min(x0, x1);\n",
+       "    var min_y = Math.min(y0, y1);\n",
+       "    var width = Math.abs(x1 - x0);\n",
+       "    var height = Math.abs(y1 - y0);\n",
+       "\n",
+       "    fig.rubberband_context.clearRect(\n",
+       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
+       "\n",
+       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
+       "    // Updates the figure title.\n",
+       "    fig.header.textContent = msg['label'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
+       "    var cursor = msg['cursor'];\n",
+       "    switch(cursor)\n",
+       "    {\n",
+       "    case 0:\n",
+       "        cursor = 'pointer';\n",
+       "        break;\n",
+       "    case 1:\n",
+       "        cursor = 'default';\n",
+       "        break;\n",
+       "    case 2:\n",
+       "        cursor = 'crosshair';\n",
+       "        break;\n",
+       "    case 3:\n",
+       "        cursor = 'move';\n",
+       "        break;\n",
+       "    }\n",
+       "    fig.rubberband_canvas.style.cursor = cursor;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
+       "    fig.message.textContent = msg['message'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
+       "    // Request the server to send over a new figure.\n",
+       "    fig.send_draw_message();\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
+       "    fig.image_mode = msg['mode'];\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Called whenever the canvas gets updated.\n",
+       "    this.send_message(\"ack\", {});\n",
+       "}\n",
+       "\n",
+       "// A function to construct a web socket function for onmessage handling.\n",
+       "// Called in the figure constructor.\n",
+       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
+       "    return function socket_on_message(evt) {\n",
+       "        if (evt.data instanceof Blob) {\n",
+       "            /* FIXME: We get \"Resource interpreted as Image but\n",
+       "             * transferred with MIME type text/plain:\" errors on\n",
+       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
+       "             * to be part of the websocket stream */\n",
+       "            evt.data.type = \"image/png\";\n",
+       "\n",
+       "            /* Free the memory for the previous frames */\n",
+       "            if (fig.imageObj.src) {\n",
+       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
+       "                    fig.imageObj.src);\n",
+       "            }\n",
+       "\n",
+       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
+       "                evt.data);\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
+       "            fig.imageObj.src = evt.data;\n",
+       "            fig.updated_canvas_event();\n",
+       "            fig.waiting = false;\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        var msg = JSON.parse(evt.data);\n",
+       "        var msg_type = msg['type'];\n",
+       "\n",
+       "        // Call the  \"handle_{type}\" callback, which takes\n",
+       "        // the figure and JSON message as its only arguments.\n",
+       "        try {\n",
+       "            var callback = fig[\"handle_\" + msg_type];\n",
+       "        } catch (e) {\n",
+       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
+       "            return;\n",
+       "        }\n",
+       "\n",
+       "        if (callback) {\n",
+       "            try {\n",
+       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
+       "                callback(fig, msg);\n",
+       "            } catch (e) {\n",
+       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
+       "            }\n",
+       "        }\n",
+       "    };\n",
+       "}\n",
+       "\n",
+       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
+       "mpl.findpos = function(e) {\n",
+       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
+       "    var targ;\n",
+       "    if (!e)\n",
+       "        e = window.event;\n",
+       "    if (e.target)\n",
+       "        targ = e.target;\n",
+       "    else if (e.srcElement)\n",
+       "        targ = e.srcElement;\n",
+       "    if (targ.nodeType == 3) // defeat Safari bug\n",
+       "        targ = targ.parentNode;\n",
+       "\n",
+       "    // jQuery normalizes the pageX and pageY\n",
+       "    // pageX,Y are the mouse positions relative to the document\n",
+       "    // offset() returns the position of the element relative to the document\n",
+       "    var x = e.pageX - $(targ).offset().left;\n",
+       "    var y = e.pageY - $(targ).offset().top;\n",
+       "\n",
+       "    return {\"x\": x, \"y\": y};\n",
+       "};\n",
+       "\n",
+       "/*\n",
+       " * return a copy of an object with only non-object keys\n",
+       " * we need this to avoid circular references\n",
+       " * http://stackoverflow.com/a/24161582/3208463\n",
+       " */\n",
+       "function simpleKeys (original) {\n",
+       "  return Object.keys(original).reduce(function (obj, key) {\n",
+       "    if (typeof original[key] !== 'object')\n",
+       "        obj[key] = original[key]\n",
+       "    return obj;\n",
+       "  }, {});\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
+       "    var canvas_pos = mpl.findpos(event)\n",
+       "\n",
+       "    if (name === 'button_press')\n",
+       "    {\n",
+       "        this.canvas.focus();\n",
+       "        this.canvas_div.focus();\n",
+       "    }\n",
+       "\n",
+       "    var x = canvas_pos.x * mpl.ratio;\n",
+       "    var y = canvas_pos.y * mpl.ratio;\n",
+       "\n",
+       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
+       "                             step: event.step,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "\n",
+       "    /* This prevents the web browser from automatically changing to\n",
+       "     * the text insertion cursor when the button is pressed.  We want\n",
+       "     * to control all of the cursor setting manually through the\n",
+       "     * 'cursor' event from matplotlib */\n",
+       "    event.preventDefault();\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    // Handle any extra behaviour associated with a key event\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.key_event = function(event, name) {\n",
+       "\n",
+       "    // Prevent repeat events\n",
+       "    if (name == 'key_press')\n",
+       "    {\n",
+       "        if (event.which === this._key)\n",
+       "            return;\n",
+       "        else\n",
+       "            this._key = event.which;\n",
+       "    }\n",
+       "    if (name == 'key_release')\n",
+       "        this._key = null;\n",
+       "\n",
+       "    var value = '';\n",
+       "    if (event.ctrlKey && event.which != 17)\n",
+       "        value += \"ctrl+\";\n",
+       "    if (event.altKey && event.which != 18)\n",
+       "        value += \"alt+\";\n",
+       "    if (event.shiftKey && event.which != 16)\n",
+       "        value += \"shift+\";\n",
+       "\n",
+       "    value += 'k';\n",
+       "    value += event.which.toString();\n",
+       "\n",
+       "    this._key_event_extra(event, name);\n",
+       "\n",
+       "    this.send_message(name, {key: value,\n",
+       "                             guiEvent: simpleKeys(event)});\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
+       "    if (name == 'download') {\n",
+       "        this.handle_save(this, null);\n",
+       "    } else {\n",
+       "        this.send_message(\"toolbar_button\", {name: name});\n",
+       "    }\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
+       "    this.message.textContent = tooltip;\n",
+       "};\n",
+       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
+       "\n",
+       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
+       "\n",
+       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
+       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
+       "    // object with the appropriate methods. Currently this is a non binary\n",
+       "    // socket, so there is still some room for performance tuning.\n",
+       "    var ws = {};\n",
+       "\n",
+       "    ws.close = function() {\n",
+       "        comm.close()\n",
+       "    };\n",
+       "    ws.send = function(m) {\n",
+       "        //console.log('sending', m);\n",
+       "        comm.send(m);\n",
+       "    };\n",
+       "    // Register the callback with on_msg.\n",
+       "    comm.on_msg(function(msg) {\n",
+       "        //console.log('receiving', msg['content']['data'], msg);\n",
+       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
+       "        ws.onmessage(msg['content']['data'])\n",
+       "    });\n",
+       "    return ws;\n",
+       "}\n",
+       "\n",
+       "mpl.mpl_figure_comm = function(comm, msg) {\n",
+       "    // This is the function which gets called when the mpl process\n",
+       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
+       "\n",
+       "    var id = msg.content.data.id;\n",
+       "    // Get hold of the div created by the display call when the Comm\n",
+       "    // socket was opened in Python.\n",
+       "    var element = $(\"#\" + id);\n",
+       "    var ws_proxy = comm_websocket_adapter(comm)\n",
+       "\n",
+       "    function ondownload(figure, format) {\n",
+       "        window.open(figure.imageObj.src);\n",
+       "    }\n",
+       "\n",
+       "    var fig = new mpl.figure(id, ws_proxy,\n",
+       "                           ondownload,\n",
+       "                           element.get(0));\n",
+       "\n",
+       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
+       "    // web socket which is closed, not our websocket->open comm proxy.\n",
+       "    ws_proxy.onopen();\n",
+       "\n",
+       "    fig.parent_element = element.get(0);\n",
+       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
+       "    if (!fig.cell_info) {\n",
+       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    var output_index = fig.cell_info[2]\n",
+       "    var cell = fig.cell_info[0];\n",
+       "\n",
+       "};\n",
+       "\n",
+       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
+       "    var width = fig.canvas.width/mpl.ratio\n",
+       "    fig.root.unbind('remove')\n",
+       "\n",
+       "    // Update the output cell to use the data from the current canvas.\n",
+       "    fig.push_to_output();\n",
+       "    var dataURL = fig.canvas.toDataURL();\n",
+       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
+       "    // the notebook keyboard shortcuts fail.\n",
+       "    IPython.keyboard_manager.enable()\n",
+       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
+       "    fig.close_ws(fig, msg);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
+       "    fig.send_message('closing', msg);\n",
+       "    // fig.ws.close()\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
+       "    // Turn the data on the canvas into data in the output cell.\n",
+       "    var width = this.canvas.width/mpl.ratio\n",
+       "    var dataURL = this.canvas.toDataURL();\n",
+       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.updated_canvas_event = function() {\n",
+       "    // Tell IPython that the notebook contents must change.\n",
+       "    IPython.notebook.set_dirty(true);\n",
+       "    this.send_message(\"ack\", {});\n",
+       "    var fig = this;\n",
+       "    // Wait a second, then push the new image to the DOM so\n",
+       "    // that it is saved nicely (might be nice to debounce this).\n",
+       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._init_toolbar = function() {\n",
+       "    var fig = this;\n",
+       "\n",
+       "    var nav_element = $('<div/>')\n",
+       "    nav_element.attr('style', 'width: 100%');\n",
+       "    this.root.append(nav_element);\n",
+       "\n",
+       "    // Define a callback function for later on.\n",
+       "    function toolbar_event(event) {\n",
+       "        return fig.toolbar_button_onclick(event['data']);\n",
+       "    }\n",
+       "    function toolbar_mouse_event(event) {\n",
+       "        return fig.toolbar_button_onmouseover(event['data']);\n",
+       "    }\n",
+       "\n",
+       "    for(var toolbar_ind in mpl.toolbar_items){\n",
+       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
+       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
+       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
+       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
+       "\n",
+       "        if (!name) { continue; };\n",
+       "\n",
+       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
+       "        button.click(method_name, toolbar_event);\n",
+       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
+       "        nav_element.append(button);\n",
+       "    }\n",
+       "\n",
+       "    // Add the status bar.\n",
+       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
+       "    nav_element.append(status_bar);\n",
+       "    this.message = status_bar[0];\n",
+       "\n",
+       "    // Add the close button to the window.\n",
+       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
+       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
+       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
+       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
+       "    buttongrp.append(button);\n",
+       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
+       "    titlebar.prepend(buttongrp);\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._root_extra_style = function(el){\n",
+       "    var fig = this\n",
+       "    el.on(\"remove\", function(){\n",
+       "\tfig.close_ws(fig, {});\n",
+       "    });\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
+       "    // this is important to make the div 'focusable\n",
+       "    el.attr('tabindex', 0)\n",
+       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
+       "    // off when our div gets focus\n",
+       "\n",
+       "    // location in version 3\n",
+       "    if (IPython.notebook.keyboard_manager) {\n",
+       "        IPython.notebook.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "    else {\n",
+       "        // location in version 2\n",
+       "        IPython.keyboard_manager.register_events(el);\n",
+       "    }\n",
+       "\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
+       "    var manager = IPython.notebook.keyboard_manager;\n",
+       "    if (!manager)\n",
+       "        manager = IPython.keyboard_manager;\n",
+       "\n",
+       "    // Check for shift+enter\n",
+       "    if (event.shiftKey && event.which == 13) {\n",
+       "        this.canvas_div.blur();\n",
+       "        // select the cell after this one\n",
+       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
+       "        IPython.notebook.select(index + 1);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
+       "    fig.ondownload(fig, null);\n",
+       "}\n",
+       "\n",
+       "\n",
+       "mpl.find_output_cell = function(html_output) {\n",
+       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
+       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
+       "    // IPython event is triggered only after the cells have been serialised, which for\n",
+       "    // our purposes (turning an active figure into a static one), is too late.\n",
+       "    var cells = IPython.notebook.get_cells();\n",
+       "    var ncells = cells.length;\n",
+       "    for (var i=0; i<ncells; i++) {\n",
+       "        var cell = cells[i];\n",
+       "        if (cell.cell_type === 'code'){\n",
+       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
+       "                var data = cell.output_area.outputs[j];\n",
+       "                if (data.data) {\n",
+       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
+       "                    data = data.data;\n",
+       "                }\n",
+       "                if (data['text/html'] == html_output) {\n",
+       "                    return [cell, data, j];\n",
+       "                }\n",
+       "            }\n",
+       "        }\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "// Register the function which deals with the matplotlib target/channel.\n",
+       "// The kernel may be null if the page has been refreshed.\n",
+       "if (IPython.notebook.kernel != null) {\n",
+       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
+       "}\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Javascript object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
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QUfQEXQEfSkFvVFko73nyGzpS9wR9KErWPv6I+jtWS3Llgg6go6gI+gIOoKOoCPoCDqCbgm6I8BtZnJn3ZicF6M59YuyHUF3jsuJWWXDDPqyuNimx4Ggb4po6TZA0BF0BB1BR9ARdAQdQUfQEXQEvZYDzmCAI9tOjCvoR771yMb7/deO/9rq31jiXp7rIejltUnqGiHoCDqCjqAj6Ag6go6gI+gIejZBd2e1HWnOWZYj204Mgp5aj8raP4JeVnvkqA2CjqAj6Ah60YLe9C3aqPSJp32gse4XnvTpmX87yBCx2FFTXFM582Lib0OtHywmaUUuwWLWRcbJCycmxbnoLOu2BLhpeXYclPGSsyO++Y3mPnPGshzZdmIQ9ByKVE4ZCHo5bZGrJgg6go6gI+gIekXAkW0nBkHfmHJDHawoRaqGyi+FYA6VRSm5VLqgO89dWwMIkpzZeke2nRgEPZcmlVEOgl5GO+SsBYKOoCPoCDqCPhBBd1YTODHzBhCQKmaN+5o1JpfKyyUEfdImCLriO27bc0oJZc0mgKCPLzMQdAQdQUfQEXQEfacccFYGODFDXk1QyqznUGeNEXQEfebNZ84Sd2bQa8SMt7jzkrhhih6CPsx261JrBB1BR9ARdAQdQUfQjXcTIOjlCeZQBytKySVm0PPPoLtL8J3BCgS9izJtXSyCvnXst6pkBB1BR9ARdAQdQUfQEfQdOZDrhYjMoJc3wIGgI+h8Zm2rlKy5XAS9vDZJXSMEHUFH0BF0BB1BR9ARdAR9xpUw12AFM+g1+IUscXdmqK3ZcPMt8079mEFPrVVp9o+gp+Fa8l4RdAQdQUfQEXQEHUFH0BF0BF3MoNcHCuIdaTN+zgBC7KYpDkEv2ZOKqBuCXkQzZK0Ego6gI+gIOoKOoCPoCDqCjqAj6PUcMF7Cxgx6VocZTWEI+miaeseBIugIOoKOoCPoCDqCjqAj6Ai69e3v0qXUqt/qjDcz6HxmrQwxRNDLaIectUDQEXQEHUFH0BF0BB1BR9ARdAR9yWfQ2wxW8JK4nBrWriwEvR2nZdoKQUfQEXQEHUFH0BF0BB1BR9ARdARdCHp5moegl9cmqWuEoCPoCDqCjqAj6Ag6go6gI+gIOoKOoKc2L2P/CLoBbeAhCDqCjqAj6Aj6Fgj6y497wEzuJ572gcb2aIqJgKY4Jyb2d+FJn144L5yYeWWV8umppuMqpX65cqnv9o22H9tnzJxccs5ht62+cfgRM8/7I775jeburvMWcicmapDrufCcZRXGghn08swOQS+vTVLXCEFH0BF0BH1hEcspVU7nNGf93I5wLqnqm18pUuUcl9tWjlQ5ZTkx0R6l5xJtNbnEOrnk8HNiopYIeu12mGswAEFP7TqD3z+CPvgmXPgAEHQEHUFH0BH0ioAjSE7MEKTKOS4nxh1McQTErd9QBbiU1RhOWzkxbi45eeHEuPVzWDgxCPrUrRBBj9fYb1/YLAjonQCC3jvS4neIoCPoCDqCjqB3EHS3IzxU6QtUTeK3rNIy1LYao6A7beXkrRMzhIG5bEvIC5s1nnkTRNAR9EI0DkEvpCEyVgNBR9ARdAQdQUfQd8oBZ+BhWaXFkT6HnxMzBOlzjsuJycliWXMdQa9dChF0BD2jkM0rCkEvpCEyVgNBR9ARdAR9KQXdkSqn0126SPRdvxQz6E5bOcflxOSUvrHVb14ulc7CuVYMIZcQdASdl8RltLCWRSHoLUEt0WYIOoKOoCPoCDoz6L3MoJcuVdRv0swOCydmnpQOWdD7ZlHK4wgIOoKOoJdneQh6eW2SukYIOoKOoCPoCDqCjqAbn7crRaqGugIBQZ+cdqXkEoKOoCPoqdVr8f0j6IszG3oEgo6gI+gIOoJeEXBmxZyYebOKpXTUneNyYobMovS2Kr1+CDqCPvPmc/JKc98613PhUYNcZRX2wjwEvTy1Q9DLa5PUNULQEXQEHUFH0BF0ZtCZQd+RA01i3/cADIKOoCPo0s1P+mDjPfj8U47NPliBoKdWr8X3j6AvzmzoEQg6go6gI+gIOoKOoCPoCPqMK2GuwYpSVjuMddZ49kBBvMR8xs+Z4Y/dNMQh6ENXqfT1R9DTMy6tBAQdQUfQEXQEHUFH0BF0BB1BH+2ybgR9MoDADHppqiYh6OW1SeoaIegIOoKOoCPoCDqCjqAj6Ag6gl7PgUzPoDODnlp1hr9/BH34bbjoESDoCDqCjqAj6Ag6go6gI+gIOoKOoIsZ9EVVKv32CHp6xqWVgKAj6Ag6go6gI+gIOoKOoCPoCDqCjqCXZmpiiXuBTZK8Sgg6go6gI+gIOoKOoCPoCDqCXrygNy0Ht952Hu3tvPDNiZlTFkvck7vO4AtgBj1dE95X0p9Juq2kX0r6lKQ/knRercjflPSX1TYXS/pzSafW/v4YSadI2lvSmyQ9s/a3m0v6mKSjJW1f4DAQdAQdQU8s6M6niS486dO9SnPs7KBT7jlzn32X1VROFO6U5fCLspriSnlb8lDrF2zH9mbrobZV6blOLk0uyfPa6jVP/mTj/eCEU4+Z+TcnZnVHmZ67bixnE2lG0GvNnaitWOK+gEVl2hRBTwP6FpL+vZLvEOv4bsMrJIUc36Eq8hBJZ0l6g6TXS7q7pNdKepSkd0m6oaQLJD1O0n9Iio8mPr7679jFh6vYdy94CAg6go6gI+i9DgYg6O063UOVPqSqW/vCb+PlhsGeNR4Iel08VxrvSaULujMb7sSkHExB0Bc0qQybI+hpID9C0jsl7Snp6qqIB0p6b/VvV0h6qaQHSTqiVoWYPb+dpLtKupOk90k6oPr7aZJOl/QySY+WdJykBxvVR9ARdAQdQUfQKwLObL0TE8Uh6JO0GyqL0meoS68fgxXtBnuc2XAnJqX07XSTaZr9jQ3nLCFH0OsDGWm+046gGzaVOARBTwM4lp9/U9IJkv5W0nWr2e6Q4/tVRcaS9y9LemqtCg+V9PeSrlPFfEdSLIOP/w45f4qkL0r6gqR7VzPsmx1BDBLEf9Z/+8Sq05WVFW3bts1afho7alq22vdMmrM8Nmf9cpZVOotlrV8ukeibX+QmS9zXLnulS0vp9UOq2kmVM3DjxDDYs7Hbk+sa7bZVkzg3LVWPo3Nk24lB0Ke60MYScmc23IlJ2VYI+mYqlf/vCHo65veS9A+SbiBpN0n/Jul3JP20KvLsSt5fXKvC3SR9VtKBkr4nKYT9BZL2kvS2GGOU9GZJX63k/pWS9qj+/R8bDiVinj/9NwR9Z1p9C9LYBiuWlV+uzl/f/BB0pGrWPaH0wQDq1y1vS+c3xsEeBL3KaWbQd5zcCHo6+VqWPSPoaVoylqXHDPl7qqXuMWsdon2lpPtIukZSCPpbJL2kVoV4Dv0zkm4s6ZIZVfutaol7zKqfWz2vHtvFjPqtJf1gRgwz6DUofUtz7JrVBGuA+xbMvtvKrR+CPjmBcuW6O1OVq63GVr8xShW5tHbe953rY8wlBB1Bn+6bI+hp5GuZ9oqgp2nNF0q6f/WG9fUSDqqWpMfz5Z+rBH7eEvd4Tr3+C9GO7R9bif7HJe1fbRDL3mMA4P0tDodn0BsguQKXS1qo3+aiGFv0vaw7V0e97/ZNwSJXrvctBaXPKpZevzFKVa7zfmy5PsZcQtARdAS9hZ2wyQYCCHqahHi5pHtIunNt9zErHp9Si1nyf61eEhcvjrtNbZvXSbp99ZK46Zq9qFrqfmL1JvhPSLp+tdFXqmXuMWO/2Q9BR9BXCZQyQ51L+lwBLr2j7hyXExM5k6utxiYtCPrkolw6C+pHW83qQjhvZOcZ9BpJXhK3A4b1fXeDXxS4XhbPoG+mTvn/jqCnYR4fqYwZ7nj+O97mHkvc41nzw6u3tv9C0vpn1uITa/GptZhZj7e4r39mrV6z+JZ6yHfI+6WVqMcn2J5dLYWPz7LdUtJFLQ4HQUfQEfSpHJg3WIGgT2A5gu7IthMTtSy9rYZav2DLp7HWzgMEHUFH0Od8Oz3gNMkiz6DvSB13ibvzRvs2ZSHoLewp8yYIejrgvyvpWZIOlXRZ9ZK4EOp4u/v6L54lj++jh4DH7Hp8ei0kvf6LNorn0uNZ9Q/U/vCAeNFn9Yb250l6Y8tDQdARdAQdQW+8XDgrK5wBDqRvOKJDWw2nrUofQBhjLuVa4v6Nw+tf7d14iT/im99o7iIaby6XI9tOjCRHSq36rQ4uLP4ZszYCPA3fiYl9OCzalIWgtzSojJsh6BlhF1IUgo6gI+gjF3R3hpoZ9LXEcfkxgz458YbKonQBLr1+CPrkHOh7iTuCXruxm4MBCLpihGJ7Ib4y6mog6ONrfgQdQUfQl0jQHVl0YgIZgo6gT18+3VxC0MmlseQSM+hVS5vS7MwaM4M+ObuYQR+m6CHow2y3LrVG0BF0BB1Bb7yGODNwTswYZ9KGKqW01eR0IddhMeviWcJL4phBZwZ9Vm4i6F2UaetiEfStY79VJSPoCDqCjqAj6BUBZwbYiYniEPRJ2g2VBYKOoCPovCSungNtBHg6Z5yY2IezmqBNWTyDvlVK1lwugl5em6SuEYKOoCPoCDqCjqDvlAPOwIMTM+TBCgQdQc8h6M5suBOzeizGi9GsJeQscd+ROm2keVaeIeipFamc/SPo5bRFrpog6Ag6go6gI+gIOoJ+Wv3DKBtxMMO/xqPvAZjY59g+2dckzvPerG7JtinACHrt3DcGKxzZdmKilgh6LlXa+nIQ9K1vg9w1QNARdAQdQUfQEXQEHUHfkQO5pBlBn5x2CHpdjFca70mOlFoz/FEDBJ23uOe2sobyEPRCGiJjNRB0BB1BR9ARdAQdQUfQEfQZV8K+ByuYQa8gmzP8CPokSR0WbWbreQY9o4W1LApBbwlqiTZD0BF0BB1BR9ARdAQdQUfQEXRr1tiaoUbQd2RbG2medZNG0JfIxjY5FAR9PG29fqQIOoKOoCPoCDqCjqAj6Ag6go6g13OAJe4scS/ECxH0QhoiYzUQdAQdQUfQEXQEHUFH0BH0QgU95wy189x1zvo5s8ZW/SIXEHQEPaOQzSsKQS+kITJWA0FH0BF0BB1BR9ARdAQdQUfQLSm1BHgAS9ydwQBnuboTE6maqn48g57RwloWhaC3BLVEmyHoCDqCjqAj6Ag6go6gI+gLCPprnvzJxuvmCace0/g35yVxOQXYmTXOWT9HSq36JRTg6eRA0JfIqhIdCoKeCGzBu0XQEXQEHUFH0BF0BB1BR9AR9Gwz6KVJ6ayboDMY4ByXE8MMesFmlaBqCHoCqIXvEkFH0BF0BB1BR9ARdAQdQUfQEfRaDiDo4hn0QiQOQS+kITJWA0FH0BF0BB1BR9ARdAQdQUfQEXQEXTyDntHCWhaFoLcEtUSbIegIOoKOoCPoCDqCjqAj6Ag6go6gI+gFSh6CXmCjJK4Sgo6gI+gIOoKOoCPoCPpoBd154ZsTE4B5SdxampX23PWsmyBL3FnintjBWu8eQW+Namk2RNARdAQdQUfQEXQEHUFH0GdcCZveyI6g12AZn0xD0Cf8SmPBEvfyHA9BL69NUtcIQUfQEXQEHUFH0BF0BB1BzyDoR771yJnX268d/7Xm/p4hwO6nxXJ9Zq00KWUGXTr/lGNXMSDoqdVr8f0j6IszG3oEgo6gI+gIOoKOoCPoCDqCjqDzDHotB1jizhL3UiQPQS+lJfLVA0FH0BF0BB1BR9ARdAQdQUfQEXQEnRn0fA7WuiQEvTWqpdkQQUfQEXQEHUEfiKBby1ad5bGSXn7cAxbOCycmCmmKO9GQ5thfUxz1mzSpw8KJmde+pbSV8zy5ExPHyxL3tRxkifvkXCyNBUvcy3M8BL28NkldIwQdQUfQEfSFRQyp6iY6Lj8EvRv3vgWTAYRJewyZhSPbTgyCXq6UzroJssSdJe6pJazt/hH0tqSWZzsEHUFH0BF0BL0i4AicE4Ogb0w5ZtDXeOTMJacsJ8Y9rr7LmjeA4Mi2E7PayE0rWk5eae5ZOqtgnJiM9Stt1hhB5yVxJVXB6roAACAASURBVKsdgl5y66SpG4KOoI9S0HN2/pyynJghdISd43JiXAF2ynJi3Poxgz65YDvcnRi3rZyynJgh1y/qXsLjCI5sOzEI+uT8RdDLZcES9zTC1WWvCHoXesOMRdARdAR9KgecpZpOTM7Oac6ySmcx5Poh6Aj6rFvWUFcglHJdsmS78Blq61rBDPqG04sl7ixxL0XtEPRSWiJfPRB0BB1BR9AbrziOzDoxpXTUnRlMJ8ad9bQ63aZIOMflxLgsnLKcGOrXbVBkHr9SznsEvXYLyLQEnxl0ZtDzqc7wS0LQh9+Gix4Bgo6gI+gIOoJeEXAEzolxpQ9B7yaLOdvKKcuJcXPJKcuJQdCnLq+ZBNi6VjCDzgy6xGfWFjWpDNsj6BkgF1YEgo6gI+gIOoKOoO+UA46MOTFDFkxWi0zSZsgsmEFnBn3WTZAl7ixxL8XZEPRSWiJfPRB0BH3wgu5IgRMzZJGIupfwMiaHuxMz5LaaJzrWrBhL3Hdc5cmlslYglHJdQtARdASdt7jnU6/FS0LQF2c29AgEHUEvStCH+rKj0mePSukIO4LkxCDoLTrcscmcTzs53J2YZW0rh4UTM2R+pVyXEPQW14uePwPHM+gT5qWx4C3u5akdgl5em6Su0SgE3ZG+C0/6dCP7g065Z+PfmuKcmCikKW5Z6+e0ldOpdWLoCG9Me9pqjUfOXGIGvawZ4NIH5kqvXymCnvO84jvoa+dwaVLKDDoz6KmFq8v+EfQu9IYZi6Azg55kBj2ntDhlOTEIOoI+63KRM5dyioRzXE7MkM+r0gW49Poh6PWZ65XmXqTzmIoTEzXI9BI7BJ0Z9GFq09bUGkHfGu5bWSqCXoCgO53anDPoTv2cGDrq3WYH3dlc2qob95z8EPThtJWTF07MkK+bCDqCPqsLdv4pxzb2i3O9uG3eLL9Tv9hfU1xpgxUscd9KLZtdNoJeXpukrhGCPjJBZ1nyWoPTEe4mOkOWgtJnFXlJ3CQ3h9xWzjXGiRnyuYigI+gIennL/RH01Oq1+P4R9MWZDT0CQW9owdI7Su4MOoKOoE+nfOm5Tv3qnfh9m+85TS9xMpe6OtydmCELJgMIyzGYknNlSq4l5NYxRXOyxH1HUuearWcGfegqlb7+CHp6xqWVgKAj6KsE6Gh262iWzq+UmSpH4JyYZZU+q9ONoO84ucmlbitn+uZXynUp53mVS4CtY0LQN/QIEXS+g16KtCHopbREvnog6Ag6gj6VA45sOzE5O6c5yyqdxZDrZ3W6EXQEfcZ9zpFt63Nk5iNFTv3cgbmc5xWCvpaMpc0az+oKIugIej4dm18Sgl5KS+SrB4KOoCPoCHrjFceRWSeGAYR2KzhyioQjSE6MK1VOWU4M9ZvkJoJeu1SaA18IOoI+fcMtbbCCZ9DzSVjbkhD0tqSWZzsEHUFH0BF0BL0i4AicE+NKH4I+SVWHuxPjtpVTlhOTs34IOoI+82ZhDFaUJqXMoPMd9JLVDkEvuXXS1A1BH6igl96RW9b6ObMfDgsnZl5HnRnqdjPUDncnxpUqBB1Bn3XLyvXyTwQdQUfQmz+XFmycgQcnZl5ZXT8Dxwx6GuHqslcEvQu9YcYi6Ag6M+gLzKAj6JsLEkvcuw0G8Jm1bvwYjErHD0FH0BF0BH2YujPsWiPow24/p/YIOoKOoCPojdcOR7adGKSqnVQxg775ABG51C6XnJUfCDqCjqAj6I5sENONAILejd8QoxF0BB1BR9AR9IqAIy1OTBTnLEtG0BH0WSerk0tO3iLoCDqCjqAPUXaGXmcEfegtuHj9EXQEHUFH0IsWdEtKTQF2pMWJQdA3plwuwczZVk5ZToybS1ZZxsvA5tWvlNUO1jXGZOE8JrWM9SvtuetZN0E+s8Zn1hbXqjQRCHoariXvFUFH0BF0BB1BZwZ9pxxwBM6JySmY1K/bCgRLFPkO+sZzq0nsT15p7is6gwFOTNQgU/0Q9Elzl8aCl8SVp20IenltkrpGCDqCjqAnFnSnU9u3SJQyU2Udl9nRXMZZWSeXrJgBSJWTS04MAwi1C2TP52Ip1yXrHDFZ5BJg65gQ9A29AWbQmUFPLWFt94+gtyW1PNttmaA7HaULT/p0I/mDTrln499yddTpyHWcnRnAsuRcnSvn/JiXf6V0hK3jMjvCuc5765jMXLc63T3zG3Iu5WwrpywnJud9x8q/AQz2WMdlnle57iHWMSHoCLokZtDLkzwEPV2bBNsTJT1J0s0k/UDS6yS9uFbkb0r6S0m3lXSxpD+XdGrt74+RdIqkvSW9SdIza3+7uaSPSTo6zq0FDgNBZwadGXRm0BsvGc4b2Z2YedLndjQR9KpZTZFwZNGJySmY1K/jIKqZS87L5XK2lXWNMVkg6Gs5WNqy7lk3QWbQmUFfwKeSboqgp8P7Kkm/LelZkr4mrSb9DSV9vCryEElnSXqDpNdLuruk10p6lKR3VdteIOlxkv5D0gclPb7679jFh6vYdy94CIMS9Jw37NLLon7dOpouv1ydK7d+TpwTk1OqrM6zOUPtsHBiXH4WC1MknONyYlwWTllODPWr9SrMXHIE3YmJmjbFnXDqMc3dI+e4nJioQaZnvK1rRcb6IeiTdCyNBTPoC5pUhs0R9DSQj5B0pqRfl/SthiJeKulBkmLb9V/Mnt9O0l0l3UnS+yQdUP3xNEmnS3qZpEdLOk7Sg43qI+gN0OjIbY0Alz7rmatz1Xf+RWs2zWz3XRYz6JNzx2ExL8bqdJsi4eSFE4MAd7vW5uRn5d8caY66N4kzgt5xYMQ873Pd40qT0lldQWbQmUE3vCpJCIKeBOvqrPl/k/Q3kv5QUnCOmfP49x9XRX5K0pclPbVWhYdK+ntJ15F0XUnfkRTL4OO/Q86fIumLkr4g6d6SYoZ9s9+ekuI/6799JF24srKibdu2yX3GuynOeS580CKxpLN2TqfbicnZ0XTrl6vz4tbPiXNicraVKwWlD/Y49bNYmB11Jy+cmJy5RP06DgaYueTIthMTR8cMetXGZlvlusch6JNzsTQWzKBvplL5/46gp2EeM+GxNP0r1XPju0l6haSfSFpfc3W2pL+deib9bpI+K+lASd+TFML+Akl7SXpbLJSS9GZJX63k/pWS9qj+/R8bDiVinj/9NwR9Z1p05Lp15JaVX67OS9/8Bj3wZXY0HQF2uDsxrpQi6FyXZt3bc+W6lX/MoG9sMpa4r/IoTUpnnVfMoDODnkYLF98rgr44szYRMXP++5IOkxQiHr87SjpD0uHVsvf497dIeklth/Ec+mck3VjSJTMK+q1qiXvMqp9bPa8e28WM+q2rF9FNhzGDXiPiLD+dJzpup9vp4Dsx1K9b5341OlPnqu/2RdAnbd/3ed93W7HEfTnayskLJybndR1Br3UgzIHDXPcQt62c+jmy7cTME/vzTzm2sT9eelml1Y8Z9DZql3cbBD0N7z+T9Jxqdnu9hJgFv6x6cdw/S9psifsVU1UL0Y4l8Y+VdGW1ZH7/aptY9h4z7e9vcTg8g94AqfSOEvXrJtsuP6fz4nSU3Po5cU7MEKQg16xiTn5OLlkxA/g0lsPdicmZ66XXz80lJ44l7h0HAzIOIDiC6cQg6Bs7rKlm+BH0FvaUeRMEPQ3weHv7RyXdStK3qyLi5W+x5H19Vj1eEvdASbepVSE+w3b76iVx0zV7UbXUPT7ddgdJn5B0/Wqj2G8sZX9Pi8NB0BH0VQJDnlV0OrVOzCooZtB3nDG5BNjp3A9BqnLlUt/85l0v3PMqVy5Rv24Dm24uWXGmYPIMetXGJj/nuuTIthODoCPoLbxmKTdB0NM0667Vy9x+LulpkuJ/v6b6XnnIe/zWP7MWn1iLT63Fm9vj2fX1z6zVaxbfSQ/5Dnm/tBL1eEHcs6ul8PFZtltKuqjF4SDoCDqCPpUD1hLjk1eaTzejo9S3SAxZqqzOfcYXNrr1czrCVllG/s0b4BhyLvV9Xo1tYNPKv3kDm6t/a7h2mnmLoCPo0zfj0gcDSqsfM+gt7CnzJgh6OuDxordXV0vaQ6rju+Ux+73+FvcoOZ4lj5fHhYBfLClm1UPS679oo3guPZ5V/0DtDw+opD+Wvj9P0htbHgqCjqAj6Ah64+XCERAnZp70uVKQa1bWrR+CPkm7XG2FoG/OPMW5aJ0jCPqksRwWTsy8wZQ5g9COYDoxUb1Uy7pn3QRzlVUaCwS9pUFl3AxBzwi7kKIQdAQdQUfQEfSKgCVwGTvCOUXHYeHEBHoEfS0BS+dn5d886Vv9GzPoq43f8yqs3tsKQd9xn+z7hXQIeiFGVHA1EPSCGydR1RB0BB1BR9ARdAR95xwwBh5KF0zqN2lmi4WRE2vyuW9zFwZBX2ODoHcS4L6lOSrDDDqfWUvkXgvvFkFfGNngAxB0BB1BR9ARdAQdQT+t/tTYRhzM8Fc8HNFG0DcmU6YXjVqDIvPaihn0TgMIEdw0iMAM+uBdKvkBIOjJERdXAIKOoCPoCDqCjqAj6Aj6jhxofI8Egj45T/pmwQx6JwFmBn2Sml1Z8Ax6ca4mBL28NkldIwQdQUfQEXQEHUHvRdDdWTtmqNfwW8vOMz7D77avFWcKMG9xT7TagRn0TgMIzKCn1pnl3j+CvtztO+voEHQEHUFH0JdS0J23pFuCZIqEU7+copOzLAQdQd/pImSeVwh6N0Ef63PXs26CY2XBDHp5Moigl9cmqWuEoCPoCDqCjqAzg84MOkvcd+QAS9xrp0PPL7ErfWBurFKKoE+ekUfQU6vX4vtH0BdnNvQIBL0AQXdmqqyZvoxLIZe1frk6V33zizRv6nT3XVYp30F32spiYc70OfVzrhVWTCSMc1xODNelHXchK/8y8rNyInMuDfa8KuQZdAR90ikcKwsEvTy1Q9DLa5PUNULQexb0XJ2D0jtyy1q/obYvgt5iRsx9BtiU0ly5NASpYok7S9z7WuI+2PMKQd+RAs5LzpyYKNCJc2LmlcVb3FOrzvD3j6APvw0XPQIEvYlY4Z3uZRXg0jvquTp/fbcvgo6gz7zUFSIFpZ/31K/Knr7vi6uz6yuz78J9l1VIrue6h7gDc2OdNZ6VhGNlwQz6oiqVfnsEPT3j0kroRdCdG44jIE5MAHc6V+7NzWFhlWV2XhwWDncnZvXkML4R65TlxLj1c9rXrZ8T58S455VVlpnruXLJad+cudR7/RJIVenXJeqHoI9lhn+sUoqg8wx6aYJWrw+CXnLrpKkbgj7QGXS30116RzOXVFmiaA4gOG3l1s+Jc2KWVdCdtrJiMuZS7/VD0HfcNfp+38LozsUEueTcQ6xzJOPAYc76IeiTTuFYWTCDnka4uuwVQe9Cb5ixCDqCvkqglI6m07lyOrVOTM5ZT7d+TpwTg6DXLhxL2lHPKQWlDxxSP2bQmUE/trGX6zxD7cREBXJJc86ySmOBoJcndAh6eW2SukYIOoK+qaDn7Kgj6GsJ2bc0zxuE6busvgd7rPwb8gx16c/KJpj1dAS46XvXUb0TTj1m5pW99FwvvX69n4sJcsm5h1jHtaQDc7kEuDQpnXXBGCsLBD21ei2+fwR9cWZDj0DQRyboy9h5cTq1Tgwz6BtPFkeqLO5L2hEe7LmYQKqcXELQJ+ejw6+IczFBLg32vCpkYG6sUoqg8wx6yUKHoJfcOmnqhqAj6GsECukcOJ2rIjqaPfOzjsmcee+7LGbQaxcVJy+cmJyik6AsRzARdAR95u3beNEoM+gTkgg6LJhBTyNcXfaKoHehN8xYBB1BR9Cnc8ARJCdmjuj0Lc1RVJM4910Wgr4cgm4tQc242qF0QS+9ftZ533f7JhjscQZ5EXSklBl0ZtBL1jgEveTWSVM3BB1BR9AR9MariyPbTsy8AQSr87za8d939nH1PJiyrPVD0Cfp0yTbTc+6RySCXh+oajgXEfQJpEKuS8ygM1jBDHoa4eqyVwS9C71hxiLoPQu6c3OzOvh9z2QU0jlwpKqImaCe+VnHxBL3jWczgl4NvhlyNOdtybHT809peKNzxutSTgFG0KtTq+/2RdAR9Bl9sMbrC29x30DL6W+2GXhF0MsTOgS9vDZJXSMEHUFnBp0ZdGbQ1wk4AuLEDGCGv01HbqfEKZyFI/XzZsOZQa/Pkq8091ecvHBiBnBeOYPQOQfxU0nfdHJY1xcEHUFPbUWF7h9BL7RhElZrFIKe7YZo3jxy3nyzsaBzVZsZWXwGkxn0esd/cX6r0cygL/UMunPdRNAn55V1jen7us4MOjPozKA3fts90GzFagJm0BNal7lrBN0EN+AwBJ0Z9MHPoDsddSsmo/RZnWeWuG88mxF0BH3q+o6gI+gzb/nOwIMTk/Ee4t7jmEGfZMhYWSDo5Vkdgl5em6SuEYKOoCPo0zngPE/uxMyZPULQmUHfapGwlqAWLi0IOoK+1edV6St7xiqls/JirCwQ9NTqtfj+EfTFmQ09AkFH0BF0BL3xOua8kd2JiQo0xhUufe5M1VA76tFWJbwkzuHuCrrTVk5ZfQ/MFX8uzhmkdNp39ULGypkkK2ecpdbzrhXWAKD5GGHpZZVWPwS9PLVD0Mtrk9Q1QtARdAQdQc8m6FanG0GftI/DwokZwFvcnVxypDmn9CHorJzZ6hn+sc4aM4POd9BTC1eX/SPoXegNMxZBR9ARdAQdQV8n4MisEzOAmT5rVmdJWeSalUXQEXQEfWtejIagI+glaxyCXnLrpKkbgo6gFyXozui9M5NmxWSUqr476lH1puWufZc1b1mtxR3pYwZ91nXayQsnZgDnvfOdduu875vfKtuGz7P1XVbP7wmxrmUZc8mtn3MPdgbznJjAl6t+OcsqjQVL3NMIV5e9Iuhd6A0zFkFvaLecF0zrRrqknRfn5ruM/KzO8wDe4r6MbWUd04A76lH1oT6DXnpbuee9I+jWcv++7zsIem3wrYzvyDv3YKe/5MTklOacZZXGAkEvT+gQ9PLaJHWNEHQEPckMes4bjtPpdus31KWuzKDXTnRm0lpJgXWO9C1wI2srBL1+nu7b3P9x8sKJGeEAAoI+SbuxskDQU6vX4vtH0BdnNvQIBB1B31TQnY66E+OOWCPokyS2OvimVDWVxRL3joMBhYiEdQ6buZRr4Mu5Vqy2ZqY3g1vnryRm0FuIfSHnVa5ccnN9rFI6qys4VhYIenlqh6CX1yapa4SgI+gI+lQOzPucTK7OldtRtzplplQh6FXimPxy5ZKVE0v6FneXRa62cs97BB1B36krY16XxiqlCDoviUstXF32j6B3oTfMWAQdQUfQEfTmq9ecWScEHUHvSwpyCTCCPmkxnkFvIfWrqyfG9RI7BH2SF2NlwQx6eUKHoJfXJqlrhKAj6Ag6gm4JOlK1hs1aCr7a8W94xraQpbjWcZmzdrlYWMeUsa2YQe8ozRnbqvTBHrd+Y5VSZtCZQU8tXF32j6B3oTfM2FEIeq4bzrzOurVsupDRe6dT68Tk5OfWL5dIuB11q1NWuFRZx5Sxo156Lrn8rOMqPJesY8qYS72f93MGe5hB7zgYUHiu933ez+vDOOeVE+P2EUovq7T6MYNentAh6OW1SeoaIejMoDODzgw6M+jrBIxOt9u5yjXY49bPijP4raLPtJrAOqaM9csp6JbA9d2+hQxCF8Gi8JUzCPrkNrnsLBD01Oq1+P4R9MWZDT0CQUfQEXQEHUEfiKA7gunEzJupir81dVDdshD0tQRE0DvOamccTLGkfgD1y7Xi0L1W5Kpfztn60lgg6OWpHYJeXpukrhGCjqAj6AUKeu+dP2aqJq3c80yV27lypNQpy4lB0KcuCplm+BH0CXc3b53zyrre9r2aoOfrknVMc96pseyzxrO6grkGA9xcT1U/BD21ei2+fwR9cWZDj0DQEXQEHUFnBp0Z9J1ywOk0OjGrBRsC7JTlxLj1cwQJQe8u6I60OG1lxZi5bpVlDiA4/JzzyonJOauds6zSWCDo5akdgl5em6SuEYKOoCPoCDqCXhFwOkpOjCt9TllOzBBm0J3jcmLctnKkynpx2wCkz2HhtpUjmE79rJgBtJXDz2krJyanNOcsqzQWCHpq9Vp8/wj64syGHoGgFyDo1sXZHB0vfabK6Rw4HSWL+QA6Vw4LK2bILOYsJXXywolxpc8py4lB0KduDMYMv3NeLaugOznoxLhS5bSVFTOA66ZzD3bayolx27f0skqrH4Jentoh6OW1SeoaIegIOjPozKAzg84M+k454HQanZghDFY4A5uOwCHokzR0c8kRTKetrBgEfUcD52zf0ssqrX4Iemr1Wnz/CPrizIYesUHQc95wnGftnJhlHXHN2VbOzcOJcdvKYeHWL1dH3TmmuaKz2jFcmX29yrgawzqujPVz8sKJGYKUOsflxAyBRa7zHkFH0GdepJ1roBPDS+I24HcGe5xroBPj9pfalIWgl6d2CHp5bZK6Rgg6M+jMoDODbs2gO50XBL2G2lg23aZzNd2YTsy8zl/8rYTPrDnH5cS4AwhOrruCXvq56HB3YlxpcdrKimEGnRn0GXfbnLnepiwEPbV6Lb5/BH1xZkOPQNARdAQdQUfQKwJtOi99CbAzK5uzfjnLKp2FUz9H4EoXdCcncg/2lD5YkSuXnPxzBzicvHBictYvZ1mlsUDQy1M7BL28Nkldow2C7l4knBuOs1zdicl5kc1alrl8zWkrJy+cGJef0xFx6+fwc+pnxcybnVn9W79L3JexI+zkhRPjzso6ZTkxQ5Aq57icGLetnHMYQZ90edy2WsbrkpNLVgxL3Df0uZ1ccvLWiXH7S23KQtBTq9fi+0fQF2c29AgEnRl0ZtCZQWcGnRn0nXKgTUeur9UEpXeEcw3MIegI+syLsTMg78Qg6Ai6JAS9PLVD0Mtrk9Q1QtAR9MELek6RyNVRd2c/rLiMHbmc9XPaqvRcylm/EspqetZ9CDP8OXPdGeBw6ufkBG011ckw3j3htJUVg6Aj6Ah6au+y9o+gW9gGHYSgI+gIOjPozKAzg84M+inHNp4HuQQ4p1Q5ZSHotRQxBzadgUOnrawYBB1BR9CLlDoEPX2z3ErSlyVdJWm/qeIeLumFkm4p6duSnivpn2rb/JGkZ1b/+xRJr6j97c6SXivpTtW+2x4Jgo6gI+gIOoKOoCPoBQh63wI8bwWCI3B91y+Sru8vApQ+mIKgr11q+s6lvlfbzKtj32WVxoIl7m0VKt92CHpa1ntI+ldJP5R0tylBv6ukT0v6k0rKHyrpBZLuIenzko6s/vsBkqKdPiDpNySdJSn2+wVJT5L0xQUPYVCC7nQocl5ks5aVcfTeuXk4MTn5ufUrvXNlnSNmLi1jR9jJCydm9TptLHV1ynJi3A5032UNuSPsnItj44eg12fkG17iOe9asfq3rX/5p5O3TkzOPkLOskpjgaAvaFIZNkfQ00J+qaQDJX1C0l9NCfppkkKW71+rwkck/UTSoyQ9UtIzJN2l+ntI+19I+gdJz5H0a5KealQfQW+AVtoFc6dqmlI1VCkopaPu8HM66lbMADpy1nEtaa47ueRcl5wYBH3jFTfXYFTfbVXKddM5LifGlarSr0s56+fkutNWTozbvqWXVVr9EHTDphKHIOjpAB8j6Y2Sbi/pYTME/bvVkvX6svWnS3qapJtJOkLSZ6v4aKevVLPwV0j6kKSjJP3MqD6CPlBBdy/oQ5WCUjqaDr+cnaucZTkduZz1c9rKOa+cGGbQuwuww92JySkFY6tfKTPoFndz4LD066ZTP4efE5PzXMxZVmksEHTDphKHIOhpAN+geu78sZI+JelxMwT9V9W/v6NWhUdLeoukPat/e7KkkPb4hcifKunjkv5a0u6x0ElSCHvMpEc5s36xr/X9xd/3kXThysqKtm3bZj8T5HSErW+aZ7whlnbBnG5Mt35OWzllOTFDuCE6/IqQ0tXZ9a1fClkEiyYO5nORbq47ueSU5cQwg84AwqwOBLlUo5KxP5LzuomgT9p4rCwQ9DQy2GWvCHoXes2x75Z0tqSTqk2aBP14Se+s7eYxkt4k6doNu479PFhSiPu3qmfSD5L0dkmHSLp8RlxI/POn/x1B35lU3x2RIc8AOyycGAS9e+ev9I5czvqVLsCl1885h52YIZz3Y+2o9zUw7OSFE5Mzl6xrmfmWdKusjAMITls5MVnb12wr57icmJQsEPQ0Mthlrwh6F3rNsT+VdN3an4PzrtXb1uPFbm+WtNkS9+m937B6Mdy9JN1R0vOqN7jHdvESulhS/7UZVWIGvQalb2lOecHsq6M0VCnou63cG6LDL2fnKmdZjrTkrJ/TVk5eODGr5zMvidtxWXNyyeHuxAzhuj5UfsG2hLe4W3mRUYBzXjeHmkt99xHGfN4j6GlksMteEfQu9Jpj4/nx3Wp/jlnvZ1fPkF9UvQguXhIXy81/p7bdhyWF3MdL4qZ/b6ve6v5qSfHG9z+VdIdqo3ix3L2r59Q3OyKeQW8gZN2wBzDiOlQp6Pvm67avwy9n5ypnWU5HLmf9nLZy8sKJQdA3XnidXHK4OzFj7qj3NTDscHdicraVdS0z+whWWRkHEJy2cmKytq/ZVs5xOTEpWSDom6lT/r8j6HmYz1riHp9di+fG49vn762Wrr+o9pm1es3uIyn+Fp9mu1rSTSSdW7187mBJL5YU//2LFoezZYK+jDeclBfMvjpKpUtL6R11h1/OXM9ZltNWOevntJXTUXJiEHQEfdb92c0l51x0ynJi5t0X429DnUHvm0Xf36y3rrUjltJFzsdSJgxSnfcIegt7yrwJgp4H+CxBj5IfUYn3LSR9u5L1eH69/turmhk/bmqG/IlVbDx3/geSPtjyUBB0ZtDXCBTy4qxUN5ytHOCwOkrm7EfOspy2ylk/BH0t6/sWCaRqcjVZ9o56X9dNJwedmCEMkpd+3XTq57SVE5OzfXOWVRoLBL2lQWXcyc1aNgAAIABJREFUDEHPCLuQogYl6KVdxBhxHd/shyN9RUjpvEEYczAgV0fO4rd6vPvOvswyGLWDC4LZTbbh140fgz3t+FnXwMKv6/TnJm1fGgsEvRBDq1UDQS+vTVLXCEEf2Qx6Lqkq7YbT10yQI305O1c5y8qVS9YxsVRzR8r3fS4iVe2kyuHuxAx5po9cSpdLXDfLFeC++iOp7sEIemr1Wnz/CPrizIYegaAj6KsEmAlq11FC0Dfv9PSdS3Q0N2eeU3RyltV3Lo1NgEvnRy61u+9YecsMerJBytLPq671Q9DLUzsEvbw2SV0jBB1BR9CncsB6Wc+cZdOWYJqdq5xlpRq93+mUNFnkqp/VeR7wDD9SlU6qyKVug1FDXk3QVaq4bh7b2F8e23nVNZcQ9NTqtfj+EfTFmQ09AkFH0BF0BL35OmY8r921c0BHs9+OZt+dUwQdQZ91wej7vO87b6lft7wdG78xD/Yg6OWpHYJeXpukrhGCjqAj6Ag6gl4RcKTAiRly5w9B7yY68Nt4ueEza2s8xibApV83h3yN7ppLCHpq9Vp8/wj64syGHoGgI+h0DhYQdGfZtLPs3O28OGVZMQNeot218zJ9yXDbysklpywnZl7nFMFE0JlB7//zhVyXup1XffND0Fe/ghL/Z/vQRWcZ6o+gL0MrLnYMCDqCjqAj6M1XjQKWuPctmH135MZWPwS9m0jAjxl0BjjKH+BA0BH0xXQq7dYIelq+Je4dQUfQEXQEHUGvCDiy7cQMufOHYCLoCGb5gjm261LfA69DvkZ3ZcES9/J0DUEvr01S1whBR9ARdAQdQUfQd8oBp4PvxIy5IzwNfWz8GOxhsKevwZ6uUjqrHkN9DKkrCwQ9tXotvn8EfXFmQ49A0BF0BB1BR9ARdAT9lMXfnt+1I4ygN78cbWyDFeRSt8GKvvmNeeAQQS9P7RD08tokdY0QdAQdQUfQEXQEHUFH0HfkQK43qzOD3k1K4ZeOH4LOM+ipBWyR/SPoi9Bajm0RdAQdQUfQEXQEHUFH0BH0GVfCXIMVfc8AswJh0piwWIwFM+jlCR6CXl6bpK4Rgo6gI+gIOoKOoCPoCDqCjqBrrM9dz7oJjpUFgp5avRbfP4K+OLOhRyDoCDqCjqAj6Ag6go6gI+gIOoJeywEEne+glyJ5CHopLZGvHgg6go6gI+gIOoKOoCPoCDqCjqAj6GIGPZ+EtS0JQW9Lanm2Q9ARdAQdQUfQEXQEHUFH0BF0BB1BR9ALdDwEvcBGSVwlBB1BR9ARdAQdQUfQEXQEHUFH0BF0BD2xeDm7R9AdasOOQdARdAQdQUfQEXQEHUFH0BF0BB1BR9AL9DoEvcBGSVwlBB1BR9ARdAQdQUfQEXQEHUFH0BF0BD2xeDm7R9AdasOOQdARdAQdQUfQEXQEHUFH0BF0BB1BR9AL9DoEvcBGSVwlBB1BR9ARdAQdQUfQEXQEHUFH0BF0BD2xeDm7R9AdasOOQdARdAQdQUfQEXQEHUFH0BF0BB1BR9AL9DoEvcBGSVwlBB1BR9ATC/rNT/pg42l8foMUODGrhZy878Ky7ZbVFNd0TFExpywnZl5Z1G+SIrDoxgJ+3fhFdN/XQK5La23CdXOSm7BYjAXfQU9sXsbuEXQD2sBDEHQEHUFH0BceQECAF+vwzAI8VJFAqpDSWfnMYEW3vIBfWfzGfI9D0MszOwS9vDZJXSMEHUFH0BF0BL0i4My0ODFD7vwh6N1EAn4bLzfMoK/xQNC7nVd98xvyNborCwQ9tXotvn8EfXFmQ49A0BF0OgcIOoKOoO+UA87AgxMz5o7wNPSx8WOwopuUwi8dvzFflxD08tQOQS+vTVLXCEFH0BF0BB1BR9ARdF4StyMHcs1qI5jpBHNsgz1dZ41n3QSH+hhSVxYIemr1Wnz/CPrizIYegaAj6Ag6go6gI+gIOoKOoM+4EuYarOgqVWNfjdE3P2bQV184G/9n+9BFZxnqj6AvQysudgwIOoKOoCPoCDqCjqAj6Ag6gs5n1mo5wAw6gr6YUqXbGkFPx7bUPSPoCDqCjqAj6Ag6go6gI+gIOoKOoPMd9AKNDUEvsFESVwlBR9ARdAQdQUfQEXQEHUFH0BF0BB1BTyxezu4RdIfasGMQdAQdQUfQEXQEHUFH0BF0BB1BR9AR9AK9DkEvsFESVwlBR9ARdAQdQUfQEXQEHUFH0BF0BB1BTyxezu4RdIfasGMQdAQdQUfQEXQEHUFH0BF0BB1BR9AR9AK9DkEvsFESVwlBR9ARdAQdQUfQEXQEHUFH0BF0BB1BTyxezu4RdIfasGO2TNCbPl8ROIf63dGo+1g/yzF9GjjtC78JRZefTl79duns38krM//dLYtcX8M5Nn5DvkaPra36/jZ03/zIpcklufS2Glv9xtwf2b59u/bdl++gl6R3CHpJrZGnLgh6A+e+OyJju7nBr5tsu/wQ9M25cy4iBbMu+05eODFI6Ub6Qx2Qd6/RDGwOY2ATQUfQ82hYu1IQ9HaclmkrBB1BXyVAR7ObtJTCD0FH0KcvaX2LBILZ7VoBPwS9rwEicinduYigI+glyR6CXlJr5KkLgo6gI+hTOeDIthOTonOFoCPoCPpwH5PqezCllOuSc1xOzJilKvV5X3ou9V2/MecSS9zzCNgipSDoi9Bajm0RdAQdQUfQG69mTqfHiUkxWMFS0rVm7Vt0aKt0s3Z9t1Xp5yK5RC71tZqg71xH0JlBL0nzEPSSWiNPXRB0BB1BR9AR9IqAI0hOzJA7f0gVUtWXVJFL5FJfuYSg95dLzKDnEbBFSkHQF6G1HNsi6Ag6go6gI+gI+k454Aw8ODFDHqzoWwrGxg9B70+qpk/gseVS3+fimK9LCHp5goegl9cmqWuEoCPoCDqCjqAj6Ag630HfkQO53qyOoCPozKD3/xhS18EKBD21ei2+fwR9cWZDj0DQEXQEHUFH0BF0BB1BR9BnXAlzDVZ0lSpm0I9tvI+xmmCCpg0LBL08tUPQy2uT1DVC0BF0BB1BR9ARdAQdQUfQEfTGl0qObQAhUmGsLxpF0FOr1+L7R9AXZzb0CAQdQUfQl0jQ24yOM9PS70yLw3zInb+oO7OKa2fR2KSl71wnlyZXY3KpG4u++Q35Gt2VBYJentoh6OW1SeoaIegIOh1NBJ0ZdGbQmUFnBp0ZdGbQRztrPOsmyAy64ltr21OLCPvfnACCvjmjZdsCQUfQEXQEHUFH0BF0BB1BR9AR9FoOIOgIeinSh6CnaYnfkvR0SXeSFEJ8jqSXSXr7VHEPl/RCSbeU9G1Jz5X0T7Vt/kjSM6v/fYqkV9T+dmdJr63KuGqBw0DQEXQEHUFH0BF0BB1BR9ARdAQdQRdL3BewqEybIuhpQD9H0l6SPizp+5LiAciQ64dIen9V5F0lfVrSn1RS/lBJL5B0D0mfl3Rk9d8PkBTt9AFJvyHpLEl7SPqCpCdJ+uKCh4CgI+gIOoKOoCPoCDqCjqAj6Ag6go6gLyhSOTZH0HNQXivjg5WsP6Eq8rRqdv3+tSp8RNJPJD1K0iMlPUPSXaq/h7T/haR/kBQDAL8m6alG9RF0BB1BR9ARdAQdQUfQEXQEHUFH0BF0Q6ZShyDoqQlP9v8ZSZ+TFMvW4/fdala9vmw9lsU/TdLNJB0h6bOSbl/NoH9F0t0kXSHpQ5KOkvSzFtXfU1L8Z/23j6QLV1ZWtG3btsYLc2w8762QOjneIzHjd/JKY5Wct8E6MVGBXM8R5SyrdBbUb5L6DgsnZl7+zTuH+y6r6xtkpy8a1I9cmnUjcfLCieG63i3/uC5tzN5c/RFyPV3eLvs9jiXuLWwq8yYIeh7gj6ieP7+jpK9XRf5K0uMkvaNWhUdLektNqJ9cPcsem4TInyrp45L+WtLukk6uhD1m0j/VcCixzfOn/4ag70yLm1u3mxv8yuKHoE/aY9k7V6kHOMglcmlW/4LzqltewK8sfmMemEPQ88jgIqUg6IvQ8raNF8bF8+N/IOnvarsIQT9e0jtr//YYSW+SdO2GokLoHywpxP1b1TPpB1Xyf4iky2fEMYNeg9L3DXHMF/TUUtB3W41tAAGp6tb5g9/GM5zvoK/x4LrEedXXYAW51C2X+uY35v4cgu4JXsooBD0lXek3Kzk/UdLfTBW12RL36ZrdsHox3L0kxUz886o3uMd2P5R0jKSvtTgcnkFvgDQ2gev75ga/SWI5LJyYeR0KBLNb5w9+CHpfIkYukUvkUvOjh+7AV999GAR99dFVvoPeQqRybIKgp6O8PnP+bEmvmVFMvCQungf/ndrf4q3vP61eEjcd8rbqre6vlhRvfP9TSXeoNooXy91bUjynvtkPQUfQVwn0fXPrWzCpH4LZV6eWXCKXyCVPkPq+rjNYwblY6rmIoCPomwlUzr8j6Gloh5zHW9tfKelVtSJiWfuPq/8dL3yL58bj2+fvrZauv6j2mbV6ze4jKf4Wn2a7WtJNJJ0r6WGSDpb04uq/f9HicBB0BB1Bn8oBR+CcmJyd05xllc6C+iEFpUpB3wJceq5zXeJcLPVcRNAR9BYOlW0TBD0N6r+tni+f3vv/lRTyvv6Ll8eFeN9C0rcrWX/3VFB8Tz1mxo+bmiF/YhUbz53H8+0xINDmh6Aj6Ag6gt54rXA6+E4MHfXhdNRpq+G0VennIrlELiHo3mqWlAMIPIPeRp/yboOg5+VdQmkIOoKOoCPoCHpFwJnBdGJSdq6mG7Pv+iFVSFVfUkUukUt95VLfg1FDvkZ3ZYGgl6BnG+uAoJfXJqlr1Iug813PSTPBYo1F31LQ9YaTWlpKrx8dYTrCfXWEySVyiVziHlfPAed+78TklOacZZXGAkFPrV6L7x9BX5zZ0CMQ9IYWLO2COXbBLF2AS68fUoVUIVVIVWlSxXWJ61Jf16W+78EIOs+glyR4CHpJrZGnLgg6gr5KoO+bGwMck8RyWDgx7sqFvssil7p1ukvnh1R1a1/4bbzpNuU716Wy7iGlX5f6rh+CjqDn0bB2pSDo7Tgt01YIOoKOoE/lgHOjd2JydtRzllU6C+qHYJY6azc2KeW6xLlY6rmIoCPoJckegl5Sa+SpC4KOoCPoCHrj1caRWSeGjvpwOuq01XDaqvRzkVwilxD08h694Rn0PAK2SCkI+iK0lmNbBB1BR9ARdAS9IuDMYDoxQ56dQaqQqr6kilwil/rKpb4Ho4Z8je7KAkEvT/AQ9PLaJHWNEHQEHUFH0BF0BH2nHHAGHpyYMXeEp6GPjR+CjqAj6MygpxadZdg/gr4MrbjYMSDoCDqCjqAj6Ag6gn7KsY3nAZ/PXEPT9wACgo6gI+j9n1fMoC8mQkPYGkEfQiv1W0cEHUFH0BF0BB1BR9AR9B05kOvN6gg6go6gI+j9as1y7g1BX852nXdUCDqCjqAj6Ag6go6gI+gI+owrYa7Biq6zntNV73u1w9jqN2/FyLKz4Bn08mQQQS+vTVLXCEFH0BF0BB1BR9ARdAQdQUfQGx9jWHYpnXUTHOujLQh6avVafP8I+uLMhh6BoCPoCDqCjqAj6Ag6go6gI+gIei0HEHTFx9C3D110lqH+CPoytOJix4CgI+gIOoKOoCPoCDqCjqAj6Ag6gi5m0BcTqRxbI+g5KJdVBoKOoCPoCDqCjqAj6Ag6go6gI+gIOoJelqet1gZBL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5BL7BRElcJQUfQEXQEHUFH0BF0BB1BR9ARdAQdQU8sXs7uEXSH2rBjEHQEHUFH0BF0BB1BR9ARdAQdQUfQEfQCvQ5B39pG2VPSX0h6lKS9JH1C0h9IurCq1vUlvVXSvSWdLenxkr5aq/JrJX1b0ssXOAwEHUFH0BF0BB1BR9ARdAQdQUfQEXQEfQGJyrUpgp6L9OxyXifpgZIeJ+lHlWiHlB8l6arqf8f//yRJT5F0D0m/Ue3qrpJeLenO1bZtjwRBR9ARdAQdQUfQEXQEHUFH0BF0BB1Bb2tQGbdD0DPCnipqX0k/lPRfJZ1W/e1ASRdI+h1JH5X0IUnvk3SqpCMknS5pb0l7SPqipCdW/7bIUSDoCDqCjqAj6Ag6go6gI+gIOoKOoCPoi1hUpm0R9EygZxRzTLWkPWbMf1L7eyxhf4+k50t6iaRbSnq0pD+UdJykmDl/nqQbSnpai+rHMvr4z/pvn1hCf8EFF2jbtm369efHOMDs31l/dt/GvzXFOTFRSFNc6fWLusNiLU1Kb6ux1W/I59XY2qr06ya5NLkVlt5WpdePXCKXZnUsnbx1Yubl35j7c9u3b9fBBx8cCGLycHsLt2CTxAQQ9MSA5+w+pPstU/Icm39M0nmS/nt1osQy+LtLOr9a5n6FpA9Wov4/Jf12NYv++5JWZpR3ciX7W3eklAwBCEAAAhCAAAQgAAEIlEzgIEkXlVzBsdQNQd+6lm4S9H+uXvz25IaqfVLSKyXdTNIDJB0r6Q3VM+wnzoiZnkGPTWLW/scztl2dXZcUJ+jPWqLJFRPVyVWWU07O+uUsq3QW1G9yosKiGwv4dePHdQl+s7oNnFfd8gJ+3fhxXWrPL3LtYknXtOz/s1lCAgh6Qrib7LrNEvfpXTyhkvKHSXq3pI9Lije5h6S/oHq5XJcjWn0+fcElLrli4rhyleWUk7N+OcsqnQX1m5zxsOjGAn7d+HFdgt+s/gfnVbe8gF83flyXuvPr4hXEmgQQdBNcD2HrL4l7rKS/r/Z342oGe/0lcfVibiTpC9Wb3GP5yXslrc+mP0RSLGW/fcd6OTeCXDFcZDc2bi7uTjm0VfcbosPdiaGthtNWpbcvuUQuIejzO2HOOezEcC7SX+rzXOyoFoQ7BBB0h1p/MfF8eSxTj8+sxZLz+Cb6DWqfWauX9A5J/yrpr6t/fJakR1ZvgY/voMdz6yd0rJpzI8gVww2HG06fN5xceeuUkzPXc5ZVOgvqh2D2dY0hl8glconBirZdcud6kSum7TGwXc8EEPSegS64u2tLeln1lva9qre6/0H1qbX6ruJ16rGEPd7gfnX1h+tI+ltJ96tm1uOZ9h8sWP705vG8+h9Xb4+/vOW+csVEdXKV5ZSTs345yyqdBfWbnKiw6MYCft34cV2C36xuA+dVt7yAXzd+XJe682upA2zWJwEEvU+a7AsCEIAABCAAAQhAAAIQgAAEIGASQNBNcIRBAAIQgAAEIAABCEAAAhCAAAT6JICg90mTfUEAAhCAAAQgAAEIQAACEIAABEwCCLoJjjAIQAACEIAABCAAAQhAAAIQgECfBBD0PmmyLwhAAAIQgAAEIAABCEAAAhCAgEkAQTfBEQaBKQJxLsV/1t+yD6DFCBxSfb3gysXC2LpGID7X+E+SVqACAQgMjsDukrj+ec12a0k3lfQdSed6uyAKAhCAQDkEEPRy2iJnTe4j6R6S/q+kT0q6V/V5tficx/+S9JaclRlYWdGJOlnSPSX9H0nPl/TM6t/ib/9b0u9L+tXAjmurqxu8bifpG1tdkQGXD8N2jXetqfPzlpL+h6To5H9P0uskndFuV6Pc6rer+8a6TMYnPp9V4/cqSfEffrMJxKdRL5L0NUm7SnqOpKdIOqDKv7+W9FJJ1wBwJoGTqk/LRt/lepL+QdIx1ZbB7GOSHiXpp/CbSeD9kv5e0j9K+gWMLAI/qxi+SdK/WnsgCAKbEEDQx5cij60E/ExJh1Yd01dUF+vIh/8q6THV/x4fnc2P+IWVgL+9+gb9pyQdWw1wRGfrxZJeLenPN9/VKLd4d8NRP7jq9MeNL34PGyWddgf944bN9pO0vbaK4/rtdje6ra6SdGNJP5B0e0mflXS2pC9W/zsGimIA7gujI9PugOv8Hi7pNEmvlfQ5SXeU9IeSHi/pne12N7qtvl7dQ6Jj/8eSTpT0P6vBycOqf4t7ckg6v50JxCz5AyVFH+YNko6S9N9q/E6VFIyfCLyZBGKVX5zDl1bn6BsZkFw4U4Lhv0u6jaRvSQqGMbkV9xR+EOiFAILeC8ZB7eTLlaDHDMd/khSjqc+VFB2C+D2jkqOYYee3M4FvS3qqpA9IulV1cY4ZpOikxu+/SPpTSUcCr7FzEIMa50399fckva826xEdfH6zCcQgRqx+iZmj9V9cy6OTELkXs3PxeysAG3MwZiujMxXXv19KemRtxvLNlcDfH36b8vuMpE9UK4nWN/6jiued4DeTQMxaxuD4BdUsegz6xozm+i8GfP+qWpEAwp0JxPkaAxkh6nEfOV5S3FPWfyHscV4fCLzG8/fXJcVKmCdIuq2ks6rBjph4+AncNiUQgh73kBjojYGg6ANet+oXxn34I6yA2ZQhG2xCAEEfX4r8vJLHdUGKZbFHV6PRQSNufDGjdMPxoWl1xPXOVQTE/76DpG9W0fEs9VclbWu1t/Ft9LuSXlaJZP1RiiuqJe4xKs1vPoEYGHpHNWN0gqQ4p+MHw3aZs965CkEPSYqcjGve+i9m0D9adcDa7XFcW9X5fV9SDGR8qYYg5DNWH8SKDn47E7i4GgSPFQeXVPxi4Hz9F49axD3kOsCbSSBmLGMi4YOS/kNSrAqsLzOOVTExgLkv/GYSqJ+/sUEMpMUKhOMkxeM/76kGe+MRAn6zCUwzDG6xmig43ltSnOPRv4kBc34QsAgg6Ba2QQfF6OhdqpnfOJCYjYsOadzo4heCGaOpew/6KNNVPjpU8Qx/PD8Yv+jYx+zb+qzl4ZI+T+dgbgPcTNLbqhnMGH2OnEQuF8vZeN9BLIuNTkHMIEUewrAdw1jeGbMfP5R0frVcdv18Xr8GxkDRXu12N7qtonMaz/zGoxax6uURkk6vUYhrYDwusM/oyLQ74NdULzR7SPVoQDwa9aTajNsrJf2GpLu1293otooVGjHz+6DqP5F/8WherG6L/kusgPn/qtVso4PT4oCn5XI9JK53IekhmZF7u7XY11g3qT/mM83g5hXDuC/Hiwv5QcAigKBb2AYdFB2nF0l6b3UUMdMbkr7+Qpr/LCk6EDGTzm9nAjGqHEuHm5YPxxL3Z1erEuDXTCA6pfGCvVjKHi/ViyWJMfPBDPpiWROiFCP1sTQxOq4w3JxfdFDjTfdxzYtlifH2+1iRsP6LpZ+vrzr7m+9tfFsEv2C33n94uqSQyvVfvKDredXS2fHR2fyIY2b349UKg3+rRDJWIsR7EGJ1zA2q5ccx0MtvNoF4RO/JlZSHEMUMZry0MAYuYzVHPKMeg+n8dibQJOj1LWMVxznAayTQhmFcH3nRI0lkE0DQbXSDDXyopB9NPbNVP5h4Q2rMnv/JYI8wbcVj+WbMVE4/Q71eajyLFB2F+jOFaWs07L3fvXq5Ssyqx3P7CPri7Rkd+nhZUiytq6+OWXxP44iImY36Lx5PqctQLEuM5dmxjJbfzgTiXK3/4hGLuKes/+J9EvH7O+A1EtijmmULkbxF9Tb3+IJArISJrwhcCLtNCRwh6QEz+MXgB2LUjO9fJEU/kLfcb5pijRvE5EI8qneZvwsiITCfAIJOhkAAAltNIGYx41NX8Yk1Pk+31a1B+RCAAAQgAAEIQAACW0YAQd8y9BS8pARiiV1z8WwoAAAJk0lEQVS8Pfa7S3p8HFb5BMjB8ttomWtI/i1z65Z/bLE6Id6uzT24/LZa1hpyDVzWls14XAh6RtgDKSqWjcXbUWPZHb/FCcQL9+IZOF6wsji7iCD/PG71KHKwG0NysBs/8q8bP/KvGz/yrxs/8q8bv4gmB7szHP0eEPTRp8BOALiwdMsJ+MGvG4Hu0eRgN4bwg183At2iyT/4dSPQLZr868YPQe/Ojz3U3sIKjPEQ+MtNDvVGkuJFZ8wAzwZV/97vrC3iUyXxIjn4zeZH/nW/1pCD3RiSg934kX/d+JF/3fiRf934kX/d+EU0OdidIXvYhAAz6ONLkfh+41ckbW849Hhh1x0RzMbE+KWk/z3nLe7x7Ft8NgxBn42Q/Ot+zSEHuzEkB7vxI/+68SP/uvEj/7rxI/+68YtocrA7Q/aAoJMDUwTik0LxHfS3NZCJ7yifgWA25s3pkt5UfQpn1kbwm3/KkX/dL0nkYDeG5GA3fuRfN37kXzd+5F83fuRfN34RTQ52Z8geEHRyYIrA2yX9QNLTG8jE80dfrr7LCrydCfxV9U9Pa4ATnwt7Y/VNavjtTID8654V5GA3huRgN37kXzd+5F83fuRfN37kXzd+EU0OdmfIHhB0cmCKwAGS9pT0HchAYAsIkH9bAJ0iNxAgB0mIrSRA/m0lfcom/8gBCAyAAM+gD6CRqCIEIAABCEAAAhCAAAQgAAEILD8BBH3527jpCONlcEdJitHUayR9v3r2/OfjRdL6yOO8+c+S7jbF77OSPlHxbL2zkW5I/nVreHKwG7+IJgd9huSfz249kvzzGZJ/Pjvyrzu72AM52A9H9tJAAEEfX2rsLunl1ZvGry3pV9WFZo/qzZR/I+mZkq4YH5pWR3wTSR+QdKSks6qBjTiP9pf065K+KulBki5qtbfxbUT+dW9zcrAbQ3KwGz/yrxs/8q8bP/KvGz/yrxu/iCYHuzNkD5sQQNDHlyKvlPRwSSdK+qikn1YI9pN0X0kvk/RuSU0vQRsfsY1H/N5q5u2xkr43BSM+sRZvx/+ZpIeMHVTD8ZN/3RODHOzGkBzsxo/868aP/OvGj/zrxo/868YvosnB7gzZA4JODkwR+KGk4yR9soHMf6q+830jyM0kEI8A3L2aKZ+1wR0kfbqSeBDuTID8654V5GA3huRgN37kXzd+5F83fuRfN37kXzd+EU0OdmfIHhB0cmCKQFxY4tnpMxvIxHe8P4NgNuZN3NweKelfGrY4RtJpkhjgmA2I/Ot+SSIHuzEkB7vxI/+68SP/uvEj/7rxI/+68YtocrA7Q/aAoJMDUwTeL2kvSY+pnp+u//nXJP2v6ln0eI6a384EXi3pwZKeIemfJa1Um+wr6T7V8/3vkfRU4M0kQP51TwxysBtDcrAbP/KvGz/yrxs/8q8bP/KvG7+IJge7M2QPCDo5MEXgYEkfknR47SVn8Rb3eJt7vOTs3yUdK+lCyM0kcC1J8QzXEyTFy1biJXvxi3+/UtKbquf31/8djBsJkH/dM4Ic7MaQHOzGj/zrxo/868aP/OvGj/zrxm+9v0c/sDtH9jCHAC+JG2d67Fq9EO4ulZgHhUsk/Zukj0m6epxYFjrqbbXP1K3zO0PS9oX2Ms6Nyb9+2p0c9DmSgz679Ujyz2dI/vnsyL/u7Mi/7gxjD1wD++HIXmYQQNBJCwhAAAIQgAAEIAABCEAAAhCAQAEEEPQCGmGLqhAjqLNmyuPfD5L03S2q107FfuYzn9lnjz32uPFuu+0Wddvy33e/+9093/Wud932+te//srxxx//7XqFzj///Gu9/vWvv/9LXvKS+AwHvwYCl1566S577713PFqx4Rf//olPfOKABz3oQdOfsINljQA52D0dyEGfIfnns1uPJP98huSfz478684u9rBZDr7mNa+53wtf+MLX3eMe94jP7vKDwMIEEPSFkQ0+IJbkvFHSA6vl2KdKeoGkq6ojixfFXSxpt60+0muuuWbXM8444zm77bbb7+2yyy57SNryfL3yyiv3+NGPfrT/1VdfHc+fa4899rh8v/32++Huu+++yu/qq6/e7fvf//5BN77xjb+z1fxKLP/qq6/edfv27Tf4xS9+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width=\"1000\">"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = go.cap_alloc_plot(df[:-1])\n",
+    "lgd = ax.legend(loc='lower center', bbox_to_anchor=(0.5, -0.3), ncol=4)\n",
+    "ax.figure.savefig(\"/home/serenitas/edwin/PythonGraphs/cap_alloc_1.png\", bbox_extra_artists=(lgd,), bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>capital</th>\n",
+       "      <th>RMBS Bonds</th>\n",
+       "      <th>Cash</th>\n",
+       "      <th>Tranches</th>\n",
+       "      <th>RMBS Credit Hedges</th>\n",
+       "      <th>RMBS Rates Hedges</th>\n",
+       "      <th>CLO Bond</th>\n",
+       "      <th>CSO Bond</th>\n",
+       "      <th>Curve</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>periodenddate</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2013-01-31</th>\n",
+       "      <td>0.430710</td>\n",
+       "      <td>0.007972</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.561318</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-02-28</th>\n",
+       "      <td>0.176536</td>\n",
+       "      <td>0.367836</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.455628</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-03-31</th>\n",
+       "      <td>0.258533</td>\n",
+       "      <td>0.130804</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.228118</td>\n",
+       "      <td>0.382546</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-04-30</th>\n",
+       "      <td>0.380844</td>\n",
+       "      <td>0.031830</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.199250</td>\n",
+       "      <td>0.388075</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-05-31</th>\n",
+       "      <td>0.467674</td>\n",
+       "      <td>0.042528</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.161451</td>\n",
+       "      <td>0.328347</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-06-30</th>\n",
+       "      <td>0.488230</td>\n",
+       "      <td>0.060871</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.107800</td>\n",
+       "      <td>0.343099</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-07-31</th>\n",
+       "      <td>0.518012</td>\n",
+       "      <td>0.033196</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.107431</td>\n",
+       "      <td>0.341361</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-08-31</th>\n",
+       "      <td>0.446132</td>\n",
+       "      <td>0.110933</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.106167</td>\n",
+       "      <td>0.336767</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-09-30</th>\n",
+       "      <td>0.405778</td>\n",
+       "      <td>0.139638</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.094275</td>\n",
+       "      <td>0.360308</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-10-31</th>\n",
+       "      <td>0.383202</td>\n",
+       "      <td>0.149871</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.129174</td>\n",
+       "      <td>0.337753</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-11-30</th>\n",
+       "      <td>0.479083</td>\n",
+       "      <td>0.057664</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.127640</td>\n",
+       "      <td>0.335614</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2013-12-31</th>\n",
+       "      <td>0.485006</td>\n",
+       "      <td>0.051674</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.130285</td>\n",
+       "      <td>0.333034</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-31</th>\n",
+       "      <td>0.491939</td>\n",
+       "      <td>0.054231</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.128957</td>\n",
+       "      <td>0.324872</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-02-28</th>\n",
+       "      <td>0.423049</td>\n",
+       "      <td>0.088724</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.167632</td>\n",
+       "      <td>0.320596</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-03-31</th>\n",
+       "      <td>0.483410</td>\n",
+       "      <td>0.044299</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.166302</td>\n",
+       "      <td>0.305989</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-04-30</th>\n",
+       "      <td>0.360606</td>\n",
+       "      <td>0.280927</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.164339</td>\n",
+       "      <td>0.194128</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-05-31</th>\n",
+       "      <td>0.304159</td>\n",
+       "      <td>0.315501</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.195227</td>\n",
+       "      <td>0.185113</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-06-30</th>\n",
+       "      <td>0.361980</td>\n",
+       "      <td>0.094687</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.333131</td>\n",
+       "      <td>0.210202</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-07-31</th>\n",
+       "      <td>0.225219</td>\n",
+       "      <td>0.251357</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.301706</td>\n",
+       "      <td>0.221717</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-08-31</th>\n",
+       "      <td>0.304764</td>\n",
+       "      <td>0.174983</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.299244</td>\n",
+       "      <td>0.221009</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-09-30</th>\n",
+       "      <td>0.434793</td>\n",
+       "      <td>0.034651</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.304909</td>\n",
+       "      <td>0.225647</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-10-31</th>\n",
+       "      <td>0.381047</td>\n",
+       "      <td>0.116936</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.288351</td>\n",
+       "      <td>0.213666</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-11-30</th>\n",
+       "      <td>0.360866</td>\n",
+       "      <td>0.266194</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.287936</td>\n",
+       "      <td>0.085004</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-12-31</th>\n",
+       "      <td>0.376919</td>\n",
+       "      <td>0.185772</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.288473</td>\n",
+       "      <td>0.148835</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-01-31</th>\n",
+       "      <td>0.449420</td>\n",
+       "      <td>0.093696</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.300610</td>\n",
+       "      <td>0.156273</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-02-28</th>\n",
+       "      <td>0.471921</td>\n",
+       "      <td>0.114743</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.272051</td>\n",
+       "      <td>0.141285</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-03-31</th>\n",
+       "      <td>0.383291</td>\n",
+       "      <td>0.205980</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.280802</td>\n",
+       "      <td>0.129926</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-04-30</th>\n",
+       "      <td>0.458701</td>\n",
+       "      <td>0.247187</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.200670</td>\n",
+       "      <td>0.093442</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-05-31</th>\n",
+       "      <td>0.464610</td>\n",
+       "      <td>0.242466</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.200025</td>\n",
+       "      <td>0.092900</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-06-30</th>\n",
+       "      <td>0.523622</td>\n",
+       "      <td>0.051527</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.282668</td>\n",
+       "      <td>0.142183</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-07-31</th>\n",
+       "      <td>0.459878</td>\n",
+       "      <td>0.104380</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.283042</td>\n",
+       "      <td>0.152700</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-08-31</th>\n",
+       "      <td>0.429859</td>\n",
+       "      <td>0.096180</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.329721</td>\n",
+       "      <td>0.144240</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-09-30</th>\n",
+       "      <td>0.453848</td>\n",
+       "      <td>0.059773</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.020528</td>\n",
+       "      <td>0.330706</td>\n",
+       "      <td>0.135146</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-10-31</th>\n",
+       "      <td>0.468953</td>\n",
+       "      <td>0.023870</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.021436</td>\n",
+       "      <td>0.344366</td>\n",
+       "      <td>0.141375</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-11-30</th>\n",
+       "      <td>0.424273</td>\n",
+       "      <td>0.075654</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.021461</td>\n",
+       "      <td>0.342481</td>\n",
+       "      <td>0.136131</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-12-31</th>\n",
+       "      <td>0.405524</td>\n",
+       "      <td>0.091296</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.026637</td>\n",
+       "      <td>0.339917</td>\n",
+       "      <td>0.136626</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-01-31</th>\n",
+       "      <td>0.414662</td>\n",
+       "      <td>0.006686</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.027260</td>\n",
+       "      <td>0.412689</td>\n",
+       "      <td>0.138703</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-02-29</th>\n",
+       "      <td>0.435095</td>\n",
+       "      <td>0.030536</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.025918</td>\n",
+       "      <td>0.376846</td>\n",
+       "      <td>0.131605</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-03-31</th>\n",
+       "      <td>0.411884</td>\n",
+       "      <td>0.051062</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.025431</td>\n",
+       "      <td>0.380498</td>\n",
+       "      <td>0.131125</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-04-30</th>\n",
+       "      <td>0.432588</td>\n",
+       "      <td>0.194329</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.025059</td>\n",
+       "      <td>0.214491</td>\n",
+       "      <td>0.133534</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-05-31</th>\n",
+       "      <td>0.445444</td>\n",
+       "      <td>0.147014</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.022001</td>\n",
+       "      <td>0.251350</td>\n",
+       "      <td>0.134191</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-06-30</th>\n",
+       "      <td>0.457027</td>\n",
+       "      <td>0.133422</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.021101</td>\n",
+       "      <td>0.250804</td>\n",
+       "      <td>0.137646</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-07-31</th>\n",
+       "      <td>0.452871</td>\n",
+       "      <td>0.104486</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.051647</td>\n",
+       "      <td>0.251745</td>\n",
+       "      <td>0.139252</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-08-31</th>\n",
+       "      <td>0.405121</td>\n",
+       "      <td>0.267507</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.050026</td>\n",
+       "      <td>0.132435</td>\n",
+       "      <td>0.144912</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-09-30</th>\n",
+       "      <td>0.388695</td>\n",
+       "      <td>0.303241</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.048914</td>\n",
+       "      <td>0.108794</td>\n",
+       "      <td>0.150356</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-10-31</th>\n",
+       "      <td>0.405860</td>\n",
+       "      <td>0.294078</td>\n",
+       "      <td>0.017006</td>\n",
+       "      <td>0.046529</td>\n",
+       "      <td>0.041718</td>\n",
+       "      <td>0.055267</td>\n",
+       "      <td>0.132508</td>\n",
+       "      <td>0.007034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-11-30</th>\n",
+       "      <td>0.379328</td>\n",
+       "      <td>0.365734</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.021073</td>\n",
+       "      <td>0.049094</td>\n",
+       "      <td>0.052696</td>\n",
+       "      <td>0.128386</td>\n",
+       "      <td>0.003689</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2016-12-31</th>\n",
+       "      <td>0.367856</td>\n",
+       "      <td>0.396823</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.018758</td>\n",
+       "      <td>0.029970</td>\n",
+       "      <td>0.053172</td>\n",
+       "      <td>0.128509</td>\n",
+       "      <td>0.004911</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-01-31</th>\n",
+       "      <td>0.563245</td>\n",
+       "      <td>0.295935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.024109</td>\n",
+       "      <td>0.028793</td>\n",
+       "      <td>0.052865</td>\n",
+       "      <td>0.029943</td>\n",
+       "      <td>0.005110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-02-28</th>\n",
+       "      <td>0.597854</td>\n",
+       "      <td>0.326450</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.021237</td>\n",
+       "      <td>0.023703</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.025781</td>\n",
+       "      <td>0.004976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-03-31</th>\n",
+       "      <td>0.736444</td>\n",
+       "      <td>0.180335</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.027095</td>\n",
+       "      <td>0.021851</td>\n",
+       "      <td>0.023780</td>\n",
+       "      <td>0.004496</td>\n",
+       "      <td>0.006000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-04-30</th>\n",
+       "      <td>0.818492</td>\n",
+       "      <td>0.093116</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.028271</td>\n",
+       "      <td>0.023256</td>\n",
+       "      <td>0.026483</td>\n",
+       "      <td>0.003744</td>\n",
+       "      <td>0.006638</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-05-31</th>\n",
+       "      <td>0.736636</td>\n",
+       "      <td>0.189324</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.027305</td>\n",
+       "      <td>0.020525</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.004466</td>\n",
+       "      <td>0.021745</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-06-30</th>\n",
+       "      <td>0.742377</td>\n",
+       "      <td>0.149576</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.028228</td>\n",
+       "      <td>0.018609</td>\n",
+       "      <td>0.018365</td>\n",
+       "      <td>0.003300</td>\n",
+       "      <td>0.039545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-07-31</th>\n",
+       "      <td>0.809887</td>\n",
+       "      <td>0.084711</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.027730</td>\n",
+       "      <td>0.018896</td>\n",
+       "      <td>0.019405</td>\n",
+       "      <td>0.003447</td>\n",
+       "      <td>0.035925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-08-31</th>\n",
+       "      <td>0.839832</td>\n",
+       "      <td>0.061176</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.022905</td>\n",
+       "      <td>0.018036</td>\n",
+       "      <td>0.018824</td>\n",
+       "      <td>0.003345</td>\n",
+       "      <td>0.035882</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-09-30</th>\n",
+       "      <td>0.788294</td>\n",
+       "      <td>0.143878</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.030855</td>\n",
+       "      <td>0.015945</td>\n",
+       "      <td>0.016950</td>\n",
+       "      <td>0.003242</td>\n",
+       "      <td>0.000835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-10-31</th>\n",
+       "      <td>0.789222</td>\n",
+       "      <td>0.091395</td>\n",
+       "      <td>0.038326</td>\n",
+       "      <td>0.027148</td>\n",
+       "      <td>0.016110</td>\n",
+       "      <td>0.007972</td>\n",
+       "      <td>0.003367</td>\n",
+       "      <td>0.026459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-11-30</th>\n",
+       "      <td>0.788009</td>\n",
+       "      <td>0.105194</td>\n",
+       "      <td>0.033814</td>\n",
+       "      <td>0.023952</td>\n",
+       "      <td>0.014214</td>\n",
+       "      <td>0.007050</td>\n",
+       "      <td>0.002972</td>\n",
+       "      <td>0.024794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2017-12-31</th>\n",
+       "      <td>0.755255</td>\n",
+       "      <td>0.182248</td>\n",
+       "      <td>0.032354</td>\n",
+       "      <td>0.022918</td>\n",
+       "      <td>0.013600</td>\n",
+       "      <td>0.006752</td>\n",
+       "      <td>0.002844</td>\n",
+       "      <td>-0.015971</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "capital        RMBS Bonds      Cash  Tranches  RMBS Credit Hedges  \\\n",
+       "periodenddate                                                       \n",
+       "2013-01-31       0.430710  0.007972       NaN                 NaN   \n",
+       "2013-02-28       0.176536  0.367836       NaN                 NaN   \n",
+       "2013-03-31       0.258533  0.130804       NaN                 NaN   \n",
+       "2013-04-30       0.380844  0.031830       NaN                 NaN   \n",
+       "2013-05-31       0.467674  0.042528       NaN                 NaN   \n",
+       "2013-06-30       0.488230  0.060871       NaN                 NaN   \n",
+       "2013-07-31       0.518012  0.033196       NaN                 NaN   \n",
+       "2013-08-31       0.446132  0.110933       NaN                 NaN   \n",
+       "2013-09-30       0.405778  0.139638       NaN                 NaN   \n",
+       "2013-10-31       0.383202  0.149871       NaN                 NaN   \n",
+       "2013-11-30       0.479083  0.057664       NaN                 NaN   \n",
+       "2013-12-31       0.485006  0.051674       NaN                 NaN   \n",
+       "2014-01-31       0.491939  0.054231       NaN                 NaN   \n",
+       "2014-02-28       0.423049  0.088724       NaN                 NaN   \n",
+       "2014-03-31       0.483410  0.044299       NaN                 NaN   \n",
+       "2014-04-30       0.360606  0.280927       NaN                 NaN   \n",
+       "2014-05-31       0.304159  0.315501       NaN                 NaN   \n",
+       "2014-06-30       0.361980  0.094687       NaN                 NaN   \n",
+       "2014-07-31       0.225219  0.251357       NaN                 NaN   \n",
+       "2014-08-31       0.304764  0.174983       NaN                 NaN   \n",
+       "2014-09-30       0.434793  0.034651       NaN                 NaN   \n",
+       "2014-10-31       0.381047  0.116936       NaN                 NaN   \n",
+       "2014-11-30       0.360866  0.266194       NaN                 NaN   \n",
+       "2014-12-31       0.376919  0.185772       NaN                 NaN   \n",
+       "2015-01-31       0.449420  0.093696       NaN                 NaN   \n",
+       "2015-02-28       0.471921  0.114743       NaN                 NaN   \n",
+       "2015-03-31       0.383291  0.205980       NaN                 NaN   \n",
+       "2015-04-30       0.458701  0.247187       NaN                 NaN   \n",
+       "2015-05-31       0.464610  0.242466       NaN                 NaN   \n",
+       "2015-06-30       0.523622  0.051527       NaN                 NaN   \n",
+       "2015-07-31       0.459878  0.104380       NaN                 NaN   \n",
+       "2015-08-31       0.429859  0.096180       NaN                 NaN   \n",
+       "2015-09-30       0.453848  0.059773       NaN                 NaN   \n",
+       "2015-10-31       0.468953  0.023870       NaN                 NaN   \n",
+       "2015-11-30       0.424273  0.075654       NaN                 NaN   \n",
+       "2015-12-31       0.405524  0.091296       NaN                 NaN   \n",
+       "2016-01-31       0.414662  0.006686       NaN                 NaN   \n",
+       "2016-02-29       0.435095  0.030536       NaN                 NaN   \n",
+       "2016-03-31       0.411884  0.051062       NaN                 NaN   \n",
+       "2016-04-30       0.432588  0.194329       NaN                 NaN   \n",
+       "2016-05-31       0.445444  0.147014       NaN                 NaN   \n",
+       "2016-06-30       0.457027  0.133422       NaN                 NaN   \n",
+       "2016-07-31       0.452871  0.104486       NaN                 NaN   \n",
+       "2016-08-31       0.405121  0.267507       NaN                 NaN   \n",
+       "2016-09-30       0.388695  0.303241       NaN                 NaN   \n",
+       "2016-10-31       0.405860  0.294078  0.017006            0.046529   \n",
+       "2016-11-30       0.379328  0.365734  0.000000            0.021073   \n",
+       "2016-12-31       0.367856  0.396823       NaN            0.018758   \n",
+       "2017-01-31       0.563245  0.295935       NaN            0.024109   \n",
+       "2017-02-28       0.597854  0.326450       NaN            0.021237   \n",
+       "2017-03-31       0.736444  0.180335       NaN            0.027095   \n",
+       "2017-04-30       0.818492  0.093116       NaN            0.028271   \n",
+       "2017-05-31       0.736636  0.189324       NaN            0.027305   \n",
+       "2017-06-30       0.742377  0.149576       NaN            0.028228   \n",
+       "2017-07-31       0.809887  0.084711       NaN            0.027730   \n",
+       "2017-08-31       0.839832  0.061176       NaN            0.022905   \n",
+       "2017-09-30       0.788294  0.143878       NaN            0.030855   \n",
+       "2017-10-31       0.789222  0.091395  0.038326            0.027148   \n",
+       "2017-11-30       0.788009  0.105194  0.033814            0.023952   \n",
+       "2017-12-31       0.755255  0.182248  0.032354            0.022918   \n",
+       "\n",
+       "capital        RMBS Rates Hedges  CLO Bond  CSO Bond     Curve  \n",
+       "periodenddate                                                   \n",
+       "2013-01-31                   NaN       NaN  0.561318       NaN  \n",
+       "2013-02-28                   NaN       NaN  0.455628       NaN  \n",
+       "2013-03-31                   NaN  0.228118  0.382546       NaN  \n",
+       "2013-04-30                   NaN  0.199250  0.388075       NaN  \n",
+       "2013-05-31                   NaN  0.161451  0.328347       NaN  \n",
+       "2013-06-30                   NaN  0.107800  0.343099       NaN  \n",
+       "2013-07-31                   NaN  0.107431  0.341361       NaN  \n",
+       "2013-08-31                   NaN  0.106167  0.336767       NaN  \n",
+       "2013-09-30                   NaN  0.094275  0.360308       NaN  \n",
+       "2013-10-31                   NaN  0.129174  0.337753       NaN  \n",
+       "2013-11-30                   NaN  0.127640  0.335614       NaN  \n",
+       "2013-12-31                   NaN  0.130285  0.333034       NaN  \n",
+       "2014-01-31                   NaN  0.128957  0.324872       NaN  \n",
+       "2014-02-28                   NaN  0.167632  0.320596       NaN  \n",
+       "2014-03-31                   NaN  0.166302  0.305989       NaN  \n",
+       "2014-04-30                   NaN  0.164339  0.194128       NaN  \n",
+       "2014-05-31                   NaN  0.195227  0.185113       NaN  \n",
+       "2014-06-30                   NaN  0.333131  0.210202       NaN  \n",
+       "2014-07-31                   NaN  0.301706  0.221717       NaN  \n",
+       "2014-08-31                   NaN  0.299244  0.221009       NaN  \n",
+       "2014-09-30                   NaN  0.304909  0.225647       NaN  \n",
+       "2014-10-31                   NaN  0.288351  0.213666       NaN  \n",
+       "2014-11-30                   NaN  0.287936  0.085004       NaN  \n",
+       "2014-12-31                   NaN  0.288473  0.148835       NaN  \n",
+       "2015-01-31                   NaN  0.300610  0.156273       NaN  \n",
+       "2015-02-28                   NaN  0.272051  0.141285       NaN  \n",
+       "2015-03-31                   NaN  0.280802  0.129926       NaN  \n",
+       "2015-04-30                   NaN  0.200670  0.093442       NaN  \n",
+       "2015-05-31                   NaN  0.200025  0.092900       NaN  \n",
+       "2015-06-30                   NaN  0.282668  0.142183       NaN  \n",
+       "2015-07-31                   NaN  0.283042  0.152700       NaN  \n",
+       "2015-08-31                   NaN  0.329721  0.144240       NaN  \n",
+       "2015-09-30              0.020528  0.330706  0.135146       NaN  \n",
+       "2015-10-31              0.021436  0.344366  0.141375       NaN  \n",
+       "2015-11-30              0.021461  0.342481  0.136131       NaN  \n",
+       "2015-12-31              0.026637  0.339917  0.136626       NaN  \n",
+       "2016-01-31              0.027260  0.412689  0.138703       NaN  \n",
+       "2016-02-29              0.025918  0.376846  0.131605       NaN  \n",
+       "2016-03-31              0.025431  0.380498  0.131125       NaN  \n",
+       "2016-04-30              0.025059  0.214491  0.133534       NaN  \n",
+       "2016-05-31              0.022001  0.251350  0.134191       NaN  \n",
+       "2016-06-30              0.021101  0.250804  0.137646       NaN  \n",
+       "2016-07-31              0.051647  0.251745  0.139252       NaN  \n",
+       "2016-08-31              0.050026  0.132435  0.144912       NaN  \n",
+       "2016-09-30              0.048914  0.108794  0.150356       NaN  \n",
+       "2016-10-31              0.041718  0.055267  0.132508  0.007034  \n",
+       "2016-11-30              0.049094  0.052696  0.128386  0.003689  \n",
+       "2016-12-31              0.029970  0.053172  0.128509  0.004911  \n",
+       "2017-01-31              0.028793  0.052865  0.029943  0.005110  \n",
+       "2017-02-28              0.023703  0.000000  0.025781  0.004976  \n",
+       "2017-03-31              0.021851  0.023780  0.004496  0.006000  \n",
+       "2017-04-30              0.023256  0.026483  0.003744  0.006638  \n",
+       "2017-05-31              0.020525       NaN  0.004466  0.021745  \n",
+       "2017-06-30              0.018609  0.018365  0.003300  0.039545  \n",
+       "2017-07-31              0.018896  0.019405  0.003447  0.035925  \n",
+       "2017-08-31              0.018036  0.018824  0.003345  0.035882  \n",
+       "2017-09-30              0.015945  0.016950  0.003242  0.000835  \n",
+       "2017-10-31              0.016110  0.007972  0.003367  0.026459  \n",
+       "2017-11-30              0.014214  0.007050  0.002972  0.024794  \n",
+       "2017-12-31              0.013600  0.006752  0.002844 -0.015971  "
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
    "execution_count": null,
    "metadata": {
     "collapsed": true